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US Law Code

c.c. BY-SA 3.0. wikipedia.org

If you think that law isn’t written for lawyers, try reading some.  It can even start looking normal after a while (say about the length of time it takes to get through law degree).  But research on the main street impact of legal language suggests that for most people, the law is likely to be either incomprehensible or very hard to read.

This problem is a focus of a research project which a team of us at ANU and Cornell LII have been addressing over the past months (Eric McCreath (Australian National University, Research School of Computer Science), Wayne Weibel (Cornell University Law School, Legal Information Insitute), Nic Ceynowa (LII), Sara Frug (LII), Tom Bruce (LII) and myself (ANU)).  With the generous help of thousands of LII users, as part of a citizen science project, we’ve been collecting data on the readability of law as well as demographic data about the users of law.

If you are concerned about access to law, and many are, the current situation is not really good enough.  Whether you tend to ‘human rights’, ‘democratic values’, ‘economic efficiency’, ‘rule of law’ or are just wanting to make sure your hapless minions follow your every command, you’ll be able to think of a good reason why the law should be more accessible (readable) than it is.

Of course the problem has been around for a very long time, and plain language is a standing goal of many legislative drafting offices.  Reform efforts have been underway since the middle ages.  Certainly legal language has improved considerably, particularly as a result of 19th and 20th century reforms with that goal in mind.  Still, the law can’t be said to be readily accessible to the general public, in the sense of its readability.

What has changed that makes the problem more urgent today is that the general public can now at least get to the law.  That’s the revolution that’s been achieved by online publishers of the law, including the Free Access to Law Movement and official and commercial law publishers.  As the UK’s First Parliamentary Counsel observed last year:

Legislation affects us all. And increasingly, legislation is being searched for, read and used by a broad range of people. It is no longer confined to professional libraries; websites like legislation.gov.uk have made it accessible to everyone. So the digital age has made it easier for people to find the law of the land; but once they have found it, they may be baffled. The law is regarded by its users as intricate and intimidating.

In 2010, the Plain Writing Act was adopted by the US Congress with the aim of improving government writing. Sad to say, the Act itself is no model of plain language. Section, sub-section and paragraph roll on, line after line, provision after convoluted provision. In substance they say not much more than: write clearly so that the public can understand and use what you write.  Didn’t anyone see the irony?  Then again, reality check, most legislation is never read by the people who vote to make it law. Just to make sure the drawbridge was well and truly up, if you read through to the fine print at the end there is an important rider.  What happens if no one can understand what the law is supposed to mean? Well, nothing a judge can do about it.  Great aspiration, but …

A sea change could be on the way, though. The Good Law initiative is one great example of efforts to address the complexity and readability of legislation. What is significant is that how we are thinking about legal rules is changing.  Official publishers of the law are beginning to talk about the law as if it’s data.  The UK National Archives Office has even published an API — Application Programmers Interface (basically a ‘how to’ for developers who want to use the “data”).So now we’re thinking of law as data.  And we’re going to unleash computer scientists on it, to do whatever their imaginations can come up with. Bommarito and Katz‘ work on the legal code as a mathematical network is a great example of the virtually infinite possibilities.

Our own research uses the potential of computational technologies in another way. Online legal sites are not just ‘documents’.  They are places where people are actively interacting with the law. We used crowd-sourcing to engage with this audience, asking them to rate law on readability characteristics as well as exploring the demographics of who uses the law. Our aim was to develop a labelled dataset that could be used as input to machine learning. “Labelled data” is machine learning gold — hard to get, but if you can you get it, you can use it to make predictions about what human judges would say. In our case we are trying to predict whether a legal sentence will be readable or not.

In the process we learned quite a bit about the audience using the law, and about which law they use. Scouring the Google Analytics data, it became obvious that the law is not equally read. We may all be equal before the law, but the law is not equal before us. Just 37 sections of the US Code account for almost 10% of the page visits to US code pages (there are about 65,000). So a tiny fraction of the Code is being read all the time.  On the other hand there are huge swathes of the Code that hardly ever see the light of a back illuminated screen. This is not trivial news. Computer scientists love lists. Prioritised lists get their own special lectures for first year CS students — and here we have a prioritized list. You want to know what law is at the top of your priority list — the users will tell you. If you’re concerned with cleaning up the law code or making it easier to understand, there’s useful stuff here.

Ranking of sections by frequency of readership (on a logarithmic scale)

Ranking of sections by frequency of readership (on a logarithmic scale)

It will be no surprise that we found that law is harder for just about every part of the community than legal professionals.  What was surprising was that legal professionals (including law students), turn out to be a minority of those interested enough to respond, on the LII site at least.

These were just a few of the demographic insights we were able to draw.

On the machine learning front, we were able to show that machine learning can improve on traditional readability metrics  in predicting language difficulty (they’ve long been regarded as suspect in application to legal texts anyway). That said, it’s early days and we would like to extend the research we have done so far. There is a lot of potential for future research applying computational techniques to the readability of law.  A co-authored publication further describing the research introduced in this article will be presented at this year’s Law Via the Internet Conference being held at the end of September.

But while we’re thinking about it, there are other ways to think about `access’ to law.  What if instead of writing the law, it was visualized?  You know — like in pictures.  Before you storm off in contempt, note this: research is validating that pictures can improve user experience — for example in the contract space, where what your clients think of your contract can impact on your bottom line.

It’s radical enough unleashing computer scientists on legal rules. What might the law look like if we try thinking like designers?   ‘User experience’ of legal rules? That one didn’t come up in law school.  We’re in some surreally different world at this point. Designers create artefacts for people to use which are optimised for functionality, beauty and other characteristics –- not things that are meant to tell people what to do. ‘User experience’ is their kind of thinking.

As readers of Vox Pop will know, the idea of legal design is starting to get traction. Helena Haapio and Stefania Passera’s great article on legal design covers some of the field. An article they jointly published last year points out some of the benefits of visualization. Earlier this year, we worked on a joint paper exploring the feasibility of automating legal visualization. We were able to demonstrate the automation of visualization of clauses, such as a contract term clause, a liquidated damages clause or a payment clause. Visit our proof of concept site, where you can play with visualizing different options.

OK. So perhaps some of the above reads like we’re on the up-slope of the hype curve. But that of course is the fun. For those of us who’ve spent many years in the law, looking at the law from a different professional paradigm can help us see things that didn’t stand out before. It certainly enjoyable and brings a breath of fresh air to the law.

Michael CurtottiMichael Curtotti is undertaking a PhD in the Research School of Computer Science at the Australian National University.  His co-authored publications on legal informatics include: A Right to Access Implies a Right to Know:  An Open Online Platform for Readability ResearchEnhancing the Visualization of Law and A corpus of Australian contract language: description, profiling and analysis.  He holds a Bachelor of Laws and a Bachelor of Commerce from the University of New South Wales, and a Masters of International Law from the Australian National University.  He works part-time as a legal adviser to the ANU Students Association and the ANU Post-graduate & research students Association, providing free legal services to ANU students.

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Other related posts on VoxPopuLII on this topic include Law in the Last-Mile: The Potential of Mobile Integration into Legal Services by Sean Martin McDonald, Incomprehension Compounded by Mistranslation – The Imperatives of Access to Legal Information in South Africa by Eve Gray and Accessible Law by Nick Holmes

VoxPopuLII is edited by Judith Pratt. Editors-in-Chief are Stephanie Davidson and Christine Kirchberger, to whom queries should be directed.

1. The Death and Life of Great Legal Data Standards

VOX.open.for.businessThanks to the many efforts of the open government movement in the past decade, the benefits of machine-readable legal data — legal data which can be processed and easily interpreted by computers — are now widely understood. In the world of government statutes and reports, machine-readability would significantly enhance public transparency, help to increase efficiencies in providing services to the public, and make it possible for innovators to develop third-party services that enhance civic life.

In the universe of private legal data — that of contracts, briefs, and memos — machine-readability would open up vast potential efficiencies within the law firm context, allow the development of novel approaches to processing the law, and would help to drive down the costs of providing legal services.

However, while the benefits are understood, by and large the vision of rendering the vast majority of legal documents into a machine-readable standard has not been realized. While projects do exist to acquire and release statutory language in a machine-readable format (and the government has backed similar initiatives), the vast body of contractual language and other private legal documents remains trapped in a closed universe of hard copies, PDFs, unstructured plaintext and Microsoft Word files.

Though this is a relatively technical point, it has broad policy implications for society at large. Perhaps the biggest upshot is that machine-readability promises to vastly improve access to the legal system, not only for those seeking legal services, but also for those seeking to provide legal services, as well.

It is not for lack of a standard specification that the status quo exists. Indeed, projects like LegalXML have developed specifications that describe a machine-readable markup for a vast range of different types of legal documents. As of writing, the project includes technical committees working on legislative documents, contracts, court filings, citations, and more.

However, by and large these efforts to develop machine-readable specifications for legal data have only solved part of the problem. Creating the standard is one thing, but actually driving adoption of a legal data standard is another (often more difficult) matter. There are a number of reasons why existing standards have failed to gain traction among the creators of legal data.

For one, the oft-cited aversion of lawyers to technology remains a relevant factor. Particularly in the case of the standardization of legal data, where the projected benefits exist in the future and the magnitude of benefit speculative at the present moment, persuading lawyers and legislatures to adopt a new standard remains a challenge, at best.VOX.confidential.stamp-pdf-file

Secondly, the financial incentives of some actors may actually be opposed towards rendering the universe of legal documents into a machine-readable standard. A universe of largely machine-readable legal documents would also be one in which it may be possible for third-parties to develop systems that automate and significantly streamline legal services. In the context of the ever-present billable hour, parties may resist the introduction of technological shifts that enable these efficiencies to emerge.

Third, the costs of converting existing legal data into a machine-readable standard may also pose a significant barrier to adoption. Marking up unstructured legal text can be highly costly depending on the intended machine usage of the document and the type of document in question. Persuading a legislature, firm, or other organization with a large existing repository of legal documents to take on large one-time costs to render the documents into a common standard also discourages adoption.

These three reinforcing forces erect a significant cultural and economic barrier against the integration of machine-readable standards into the production of legal text. To the extent that one believes in the benefits from standardization for the legal industry and society at large, the issue is — in fact — not how to define a standard, but how to establish one.

2. Rough Consensus, Running Standards

So, how might one go about promulgating a standard? Particularly in a world in which lawyers, the very actors that produce the bulk of legal data, are resistant to change, mere attempts to mobilize the legal community to action are destined to fail in bringing about the fundamental shift necessary to render most if not all legal documents in a common machine-readable format.

In such a context, implementing a standard in a way that removes humans from the loop entirely may, in fact, be more effective. To do so, one might design code that was capable of automatically rendering legal text into a machine-readable format. This code could then be implemented by applications of all kinds, which would output legal documents in a standard format by default. This would include the word processors used by lawyers, but also integration with platforms like LegalZoom or RocketLawyer that routinely generate large quantities of legal data. Such a solution would eliminate the need for lawyer involvement from the process of implementing a standard entirely: any text created would be automatically parsed and outputted in a machine readable format. Scripts might also be written to identify legal documents online and process them into a common format. As the body of documents rendered in a given format grew, it would be possible for others to write software leveraging the increased penetration of the standard.

There are — obviously — technical limitations in realizing this vision of a generalized legal data parser. For one, designing a truly comprehensive parser is a massively difficult computer science challenge. Legal documents come in a vast diversity of flavors, and no common textual conventions allow for the perfect accurate parsing of the semantic content of any given legal text. Quite simply, any parser will be an imperfect (perhaps highly imperfect) approximation of full machine-readability.

Despite the lack of a perfect solution, an open question exists as to whether or not an extremely rough parsing system, implemented at sufficient scale, would be enough to kickstart the creation of a true common standard for legal text. A popular solution, however imperfect, would encourage others to implement nuances to the code. It would also encourage the design of applications for documents rendered in the standard. Beginning from the roughest of parsers, a functional standard might become the platform for a much bigger change in the nature of legal documents. The key is to achieve the “minimal viable standard” that will begin the snowball rolling down the hill: the point at which the parser is rendering sufficient legal documents in a common format that additional value can be created by improving the parser and applying it to an ever broader scope of legal data.

But, what is the critical mass of documents one might need? How effective would the parser need to be in order to achieve the initial wave of adoption? Discovering this, and learning whether or not such a strategy would be effective, is at the heart of the Restatement project.

3. Introducing Project Restatement

Supported by a grant from the Knight Foundation Prototype Fund, Restatement is a simple, rough-and-ready system which automatically parses legal text into a basic machine-readable JSON format. It has also been released under the permissive terms of the MIT License, to encourage active experimentation and implementation.

The concept is to develop an easily-extensible system which parses through legal text and looks for some common features to render into a standard format. Our general design principle in developing the parser was to begin with only the most simple features common to nearly all legal documents. This includes the parsing of headers, section information, and “blanks” for inputs in legal documents like contracts. As a demonstration of the potential application of Restatement, we’re also designing a viewer that takes documents rendered in the Restatement format and displays them in a simple, beautiful, web-readable version.

Underneath the hood, Restatement is all built upon web technology. This was a deliberate choice, as Restatement aims to provide a usable alternative to document formats like PDF and Microsoft Word. We want to make it easy for developers to write software that displays and modifies legal documents in the browser.

In particular, Restatement is built entirely in JavaScript. The past few years have been exciting for the JavaScript community. We’ve seen an incredible flourishing of not only new projects built on JavaScript, but also new tools for building cool new things with JavaScript. It seemed clear to us that it’s the platform to build on right now, so we wrote the Restatement parser and viewer in JavaScript, and made the Restatement format itself a type of JSON (JavaScript Object Notation) document.

For those who are more technically inclined, we also knew that Restatement needed a parser formalism, that is, a precise way to define how plain text can get transformed into Restatement format. We became interested in recent advance in parsing technology, called PEG (Parsing Expression Grammar).

PEG parsers are different from other types of parsers; they’re unambiguous. That means that plain text passing through a PEG parser has only one possible valid parsed output. We became excited about using the deterministic property of PEG to mix parsing rules and code, and that’s when we found peg.js.

With peg.js, we can generate a grammar that executes JavaScript code as it parses your document. This hybrid approach is super powerful. It allows us to have all of the advantages of using a parser formalism (like speed and unambiguity) while also allowing us to run custom JavaScript code on each bit of your document as it parses. That way we can use an external library, like the Sunlight Foundation’s fantastic citation, from inside the parser.

Our next step is to prototype an “interactive parser,” a tool for attorneys to define the structure of their documents and see how they parse. Behind the scenes, this interactive parser will generate peg.js programs and run them against plaintext without the user even being aware of how the underlying parser is written. We hope that this approach will provide users with the right balance of power and usability.

4. Moving Forwards

Restatement is going fully operational in June 2014. After launch, the two remaining challenges are to (a) continuing expanding the range of legal document features the parser will be able to successfully process, and (b) begin widely processing legal documents into the Restatement format.

For the first, we’re encouraging a community of legal technologists to play around with Restatement, break it as much as possible, and give us feedback. Running Restatement against a host of different legal documents and seeing where it fails will expose the areas that are necessary to bolster the parser to expand its potential applicability as far as possible.

For the second, Restatement will be rendering popular legal documents in the format, and partnering with platforms to integrate Restatement into the legal content they produce. We’re excited to say on launch Restatement will be releasing the standard form documents used by the startup accelerator Y Combinator, and Series Seed, an open source project around seed financing created by Fenwick & West.

It is worth adding that the Restatement team is always looking for collaborators. If what’s been described here interests you, please drop us a line! I’m available at tim@robotandhwang.org, and on Twitter @RobotandHwang.

 

JasonBoehmigJason Boehmig is a corporate attorney at Fenwick & West LLP, a law firm specializing in technology and life science matters. His practice focuses on startups and venture capital, with a particular emphasis on early stage issues. He is an active maintainer of the Series Seed Documents, an open source set of equity financing documents. Prior to attending law school, Jason worked for Lehman Brothers, Inc. as an analyst and then as an associate in their Fixed Income Division.

tim-hwangTim Hwang currently serves as the managing human partner at the offices of Robot, Robot & Hwang LLP. He is curator and chair for the Stanford Center on Legal Informatics FutureLaw 2014 Conference, and organized the New and Emerging Legal Infrastructures Conference (NELIC) at Berkeley Law in 2010. He is also the founder of the Awesome Foundation for the Arts and Sciences, a distributed, worldwide philanthropic organization founded to provide lightweight grants to projects that forward the interest of awesomeness in the universe. Previously, he has worked at the Berkman Center for Internet and Society at Harvard University, Creative Commons, Mozilla Foundation, and the Electronic Frontier Foundation. For his work, he has appeared in the New York Times, Forbes, Wired Magazine, the Washington Post, the Atlantic Monthly, Fast Company, and the Wall Street Journal, among others. He enjoys ice cream.

Paul_SawayaPaul Sawaya is a software developer currently working on Restatement, an open source toolkit to parse, manipulate, and publish legal documents on the web. He previously worked on identity at Mozilla, and studied computer science at Hampshire College.

VoxPopuLII is edited by Judith Pratt. Editors-in-Chief are Stephanie Davidson and Christine Kirchberger, to whom queries should be directed.

Prosumption: shifting the barriers between information producers and consumers

One of the major revolutions of the Internet era has been the shifting of the frontiers between producers and consumers [1]. Prosumption refers to the emergence of a new category of actors who not only consume but also contribute to content creation and sharing. Under the umbrella of Web 2.0, many sites indeed enable users to share multimedia content, data, experiences [2], views and opinions on different issues, and even to act cooperatively to solve global problems [3]. Web 2.0 has become a fertile terrain for the proliferation of valuable user data enabling user profiling, opinion mining, trend and crisis detection, and collective problem solving [4].

The private sector has long understood the potentialities of user data and has used them for analysing customer preferences and satisfaction, for finding sales opportunities, for developing marketing strategies, and as a driver for innovation. Recently, corporations have relied on Web platforms for gathering new ideas from clients on the improvement or the development of new products and services (see for instance Dell’s Ideastorm; salesforce’s IdeaExchange; and My Starbucks Idea). Similarly, Lego’s Mindstorms encourages users to share online their projects on the creation of robots, by which the design becomes public knowledge and can be freely reused by Lego (and anyone else), as indicated by the Terms of Service. Furthermore, companies have been recently mining social network data to foresee future action of the Occupy Wall Street movement.

Even scientists have caught up and adopted collaborative methods that enable the participation of laymen in scientific projects [5].

Now, how far has government gone in taking up this opportunity?

Some recent initiatives indicate that the public sector is aware of the potential of the “wisdom of crowds.” In the domain of public health, MedWatcher is a mobile application that allows the general public to submit information about any experienced drug side effects directly to the US Food and Drug Administration. In other cases, governments have asked for general input and ideas from citizens, such as the brainstorming session organized by Obama government, the wiki launched by the New Zealand Police to get suggestions from citizens for the drafting of a new policing act to be presented to the parliament, or the Website of the Department of Transport and Main Roads of the State of Queensland, which encourages citizens to share their stories related to road tragedies.

Even in so crucial a task as the drafting of a constitution, government has relied on citizens’ input through crowdsourcing [6]. And more recently several other initiatives have fostered crowdsourcing for constitutional reform in Morocco and in Egypt .

It is thus undeniable that we are witnessing an accelerated redefinition of the frontiers between experts and non-experts, scientists and non-scientists, doctors and patients, public officers and citizens, professional journalists and street reporters. The ‘Net has provided the infrastructure and the platforms for enabling collaborative work. Network connection is hardly a problem anymore. The problem is data analysis.

In other words: how to make sense of the flood of data produced and distributed by heterogeneous users? And more importantly, how to make sense of user-generated data in the light of more institutional sets of data (e.g., scientific, medical, legal)? The efficient use of crowdsourced data in public decision making requires building an informational flow between user experiences and institutional datasets.

Similarly, enhancing user access to public data has to do with matching user case descriptions with institutional data repositories (“What are my rights and obligations in this case?”; “Which public office can help me”?; “What is the delay in the resolution of my case?”; “How many cases like mine have there been in this area in the last month?”).

From the point of view of data processing, we are clearly facing a problem of semantic mapping and data structuring. The challenge is thus to overcome the flood of isolated information while avoiding excessive management costs. There is still a long way to go before tools for content aggregation and semantic mapping are generally available. This is why private firms and governments still mostly rely on the manual processing of user input.

The new producers of legally relevant content: a taxonomy

Before digging deeper into the challenges of efficiently managing crowdsourced data, let us take a closer look at the types of user-generated data flowing through the Internet that have some kind of legal or institutional flavour.

One type of user data emerges spontaneously from citizens’ online activity, and can take the form of:

  • citizens’ forums
  • platforms gathering open public data and comments over them (see for instance data-publica )
  • legal expert blogs (blawgs)
  • or the journalistic coverage of the legal system.

User data can as well be prompted by institutions as a result of participatory governance initiatives, such as:

  • crowdsourcing (targeting a specific issue or proposal by government as an open brainstorming session)
  • comments and questions addressed by citizens to institutions through institutional Websites or through e-mail contact.

This variety of media supports and knowledge producers gives rise to a plurality of textual genres, semantically rich but difficult to manage given their heterogeneity and quick evolution.

Managing crowdsourcing

The goal of crowdsourcing in an institutional context is to extract and aggregate content relevant for the management of public issues and for public decision making. Knowledge management strategies vary considerably depending on the ways in which user data have been generated. We can think of three possible strategies for managing the flood of user data:

Pre-structuring: prompting the citizen narrative in a strategic way

A possible solution is to elicit user input in a structured way; that is to say, to impose some constraints on user input. This is the solution adopted by IdeaScale, a software application that was used by the Open Government Dialogue initiative of the Obama Administration. In IdeaScale, users are asked to check whether their idea has already been covered by other users, and alternatively to add a new idea. They are also invited to vote for the best ideas, so that it is the community itself that rates and thus indirectly filters the users’ input.

The MIT Deliberatorium, a technology aimed at supporting large-scale online deliberation, follows a similar strategy. Users are expected to follow a series of rules to enable the correct creation of a knowledge map of the discussion. Each post should be limited to a single idea, it should not be redundant, and it should be linked to a suitable part of the knowledge map. Furthermore, posts are validated by moderators, who should ensure that new posts follow the rules of the system. Other systems that implement the same idea are featurelist and Debategraph [7].

While these systems enhance the creation and visualization of structured argument maps and promote community engagement through rating systems, they present a series of limitations. The most important of these is the fact that human intervention is needed to manually check the correct structure of the posts. Semantic technologies can play an important role in bridging this gap.

Semantic analysis through ontologies and terminologies

Ontology-driven analysis of user-generated text implies finding a way to bridge Semantic Web data structures, such as formal ontologies expressed in RDF or OWL, with unstructured implicit ontologies emerging from user-generated content. Sometimes these emergent lightweight ontologies take the form of unstructured lists of terms used for tagging online content by users. Accordingly, some works have dealt with this issue, especially in the field of social tagging of Web resources in online communities. More concretely, different works have proposed models for making compatible the so-called top-down metadata structures (ontologies) with bottom-up tagging mechanisms (folksonomies).

The possibilities range from transforming folksonomies into lightly formalized semantic resources (Lux and Dsinger, 2007; Mika, 2005) to mapping folksonomy tags to the concepts and the instances of available formal ontologies (Specia and Motta, 2007; Passant, 2007). As the basis of these works we find the notion of emergent semantics (Mika, 2005), which questions the autonomy of engineered ontologies and emphasizes the value of meaning emerging from distributed communities working collaboratively through the Web.

We have recently worked on several case studies in which we have proposed a mapping between legal and lay terminologies. We followed the approach proposed by Passant (2007) and enriched the available ontologies with the terminology appearing in lay corpora. For this purpose, OWL classes were complemented with a has_lexicalization property linking them to lay terms.

The first case study that we conducted belongs to the domain of consumer justice, and was framed in the ONTOMEDIA project. We proposed to reuse the available Mediation-Core Ontology (MCO) and Consumer Mediation Ontology (COM) as anchors to legal, institutional, and expert knowledge, and therefore as entry points for the queries posed by consumers in common-sense language.

The user corpus contained around 10,000 consumer questions and 20,000 complaints addressed from 2007 to 2010 to the Catalan Consumer Agency. We applied a traditional terminology extraction methodology to identify candidate terms, which were subsequently validated by legal experts. We then manually mapped the lay terms to the ontological classes. The relations used for mapping lay terms with ontological classes are mostly has_lexicalisation and has_instance.

A second case study in the domain of consumer law was carried out with Italian corpora. In this case domain terminology was extracted from a normative corpus (the Code of Italian Consumer law) and from a lay corpus (around 4000 consumers’ questions).

In order to further explore the particularities of each corpus respecting the semantic coverage of the domain, terms were gathered together into a common taxonomic structure [8]. This task was performed with the aid of domain experts. When confronted with the two lists of terms, both laypersons and technical experts would link most of the validated lay terms to the technical list of terms through one of the following relations:

  • Subclass: the lay term denotes a particular type of legal concept. This is the most frequent case. For instance, in the class objects, telefono cellulare (cell phone) and linea telefonica (phone line) are subclasses of the legal terms prodotto (product) and servizio (service), respectively. Similarly, in the class actors agente immobiliare (estate agent) can be seen as subclass of venditore (seller). In other cases, the linguistic structures extracted from the consumers’ corpus denote conflictual situations in which the obligations have not been fulfilled by the seller and therefore the consumer is entitled to certain rights, such as diritto alla sostituzione (entitlement to a replacement). These types of phrases are subclasses of more general legal concepts such as consumer right.
  • Instance: the lay term denotes a concrete instance of a legal concept. In some cases, terms extracted from the consumer corpus are named entities that denote particular individuals, such as Vodafone, an instance of a domain actor, a seller.
  • Equivalent: a legal term is used in lay discourse. For instance, contratto (contract) or diritto di recessione (withdrawal right).
  • Lexicalisation: the lay term is a lexical variant of the legal concept. This is the case for instance of negoziante, used instead of the legal term venditore (seller) or professionista (professional).

The distribution of normative and lay terms per taxonomic level shows that, whereas normative terms populate mostly the upper levels of the taxonomy [9], deeper levels in the hierarchy are almost exclusively represented by lay terms.

Term distribution per taxonomic level

The result of this type of approach is a set of terminological-ontological resources that provide some insights on the nature of laypersons’ cognition of the law, such as the fact that citizens’ domain knowledge is mainly factual and therefore populates deeper levels of the taxonomy. Moreover, such resources can be used for the further processing of user input. However, this strategy presents some limitations as well. First, it is mainly driven by domain conceptual systems and, in a way, they might limit the potentialities of user-generated corpora. Second, they are not necessarily scalable. In other words, these terminological-ontological resources have to be rebuilt for each legal subdomain (such as consumer law, private law, or criminal law), and it is thus difficult to foresee mechanisms for performing an automated mapping between lay terms and legal terms.

Beyond domain ontologies: information extraction approaches

One of the most important limitations of ontology-driven approaches is the lack of scalability. In order to overcome this problem, a possible strategy is to rely on informational structures that occur generally in user-generated content. These informational structures go beyond domain conceptual models and identify mostly discursive, emotional, or event structures.

Discursive structures formalise the way users typically describe a legal case. It is possible to identify stereotypical situations appearing in the description of legal cases by citizens (i.e., the nature of the problem; the conflict resolution strategies, etc.). The core of those situations is usually predicates, so it is possible to formalize them as frame structures containing different frame elements. We followed such an approach for the mapping of the Spanish corpus of consumers’ questions to the classes of the domain ontology (Fernández-Barrera and Casanovas, 2011). And the same technique was applied for mapping a set of citizens’ complaints in the domain of acoustic nuisances to a legal domain ontology (Bourcier and Fernández-Barrera, 2011). By describing general structures of citizen description of legal cases we ensure scalability.

Emotional structures are extracted by current algorithms for opinion- and sentiment mining. User data in the legal domain often contain an important number of subjective elements (especially in the case of complaints and feedback on public services) that could be effectively mined and used in public decision making.

Finally, event structures, which have been deeply explored so far, could be useful for information extraction from user complaints and feedback, or for automatic classification into specific types of queries according to the described situation.

Crowdsourcing in e-government: next steps (and precautions?)

Legal prosumers’ input currently outstrips the capacity of government for extracting meaningful content in a cost-efficient way. Some developments are under way, among which are argument-mapping technologies and semantic matching between legal and lay corpora. The scalability of these methodologies is the main obstacle to overcome, in order to enable the matching of user data with open public data in several domains.

However, as technologies for the extraction of meaningful content from user-generated data develop and are used in public-decision making, a series of issues will have to be dealt with. For instance, should the system developer bear responsibility for the erroneous or biased analysis of data? Ethical questions arise as well: May governments legitimately analyse any type of user-generated content? Content-analysis systems might be used for trend- and crisis detection; but what if they are also used for restricting freedoms?

The “wisdom of crowds” can certainly be valuable in public decision making, but the fact that citizens’ online behaviour can be observed and analysed by governments without citizens’ acknowledgement poses serious ethical issues.

Thus, technical development in this domain will have to be coupled with the definition of ethical guidelines and standards, maybe in the form of a system of quality labels for content-analysis systems.

[Editor's Note: For earlier VoxPopuLII commentary on the creation of legal ontologies, see Núria Casellas, Semantic Enhancement of Legal Information… Are We Up for the Challenge? For earlier VoxPopuLII commentary on Natural Language Processing and legal Semantic Web technology, see Adam Wyner, Weaving the Legal Semantic Web with Natural Language Processing. For earlier VoxPopuLII posts on user-generated content, crowdsourcing, and legal information, see Matt Baca and Olin Parker, Collaborative, Open Democracy with LexPop; Olivier Charbonneau, Collaboration and Open Access to Law; Nick Holmes, Accessible Law; and Staffan Malmgren, Crowdsourcing Legal Commentary.]


[1] The idea of prosumption existed actually long before the Internet, as highlighted by Ritzer and Jurgenson (2010): the consumer of a fast food restaurant is to some extent as well the producer of the meal since he is expected to be his own waiter, and so is the driver who pumps his own gasoline at the filling station.

[2] The experience project enables registered users to share life experiences, and it contained around 7 million stories as of January 2011: http://www.experienceproject.com/index.php.

[3] For instance, the United Nations Volunteers Online platform (http://www.onlinevolunteering.org/en/vol/index.html) helps volunteers to cooperate virtually with non-governmental organizations and other volunteers around the world.

[4] See for instance the experiment run by mathematician Gowers on his blog: he posted a problem and asked a large number of mathematicians to work collaboratively to solve it. They eventually succeeded faster than if they had worked in isolation: http://gowers.wordpress.com/2009/01/27/is-massively-collaborative-mathematics-possible/.

[5] The Galaxy Zoo project asks volunteers to classify images of galaxies according to their shapes: http://www.galaxyzoo.org/how_to_take_part. See as well Cornell’s projects Nestwatch (http://watch.birds.cornell.edu/nest/home/index) and FeederWatch (http://www.birds.cornell.edu/pfw/Overview/whatispfw.htm), which invite people to introduce their observation data into a Website platform.

[6] http://www.participedia.net/wiki/Icelandic_Constitutional_Council_2011.

[7] See the description of Debategraph in Marta Poblet’s post, Argument mapping: visualizing large-scale deliberations (http://serendipolis.wordpress.com/2011/10/01/argument-mapping-visualizing-large-scale-deliberations-3/).

[8] Terms have been organised in the form of a tree having as root nodes nine semantic classes previously identified. Terms have been added as branches and sub-branches, depending on their degree of abstraction.

[9] It should be noted that legal terms are mostly situated at the second level of the hierarchy rather than the first one. This is natural if we take into account the nature of the normative corpus (the Italian consumer code), which contains mostly domain specific concepts (for instance, withdrawal right) instead of general legal abstract categories (such as right and obligation).

REFERENCES

Bourcier, D., and Fernández-Barrera, M. (2011). A frame-based representation of citizen’s queries for the Web 2.0. A case study on noise nuisances. E-challenges conference, Florence 2011.

Fernández-Barrera, M., and Casanovas, P. (2011). From user needs to expert knowledge: Mapping laymen queries with ontologies in the domain of consumer mediation. AICOL Workshop, Frankfurt 2011.

Lux, M., and Dsinger, G. (2007). From folksonomies to ontologies: Employing wisdom of the crowds to serve learning purposes. International Journal of Knowledge and Learning (IJKL), 3(4/5): 515-528.

Mika, P. (2005). Ontologies are us: A unified model of social networks and semantics. In Proc. of Int. Semantic Web Conf., volume 3729 of LNCS, pp. 522-536. Springer.

Passant, A. (2007). Using ontologies to strengthen folksonomies and enrich information retrieval in Weblogs. In Int. Conf. on Weblogs and Social Media, 2007.

Poblet, M., Casellas, N., Torralba, S., and Casanovas, P. (2009). Modeling expert knowledge in the mediation domain: A Mediation Core Ontology, in N. Casellas et al. (Eds.), LOAIT- 2009. 3rd Workshop on Legal Ontologies and Artificial Intelligence Techniques joint with 2nd Workshop on Semantic Processing of Legal Texts. Barcelona, IDT Series n. 2.

Ritzer, G., and Jurgenson, N. (2010). Production, consumption, prosumption: The nature of capitalism in the age of the digital “prosumer.” In Journal of Consumer Culture 10: 13-36.

Specia, L., and Motta, E. (2007). Integrating folksonomies with the Semantic Web. Proc. Euro. Semantic Web Conf., 2007.

Meritxell Fernández-Barrera is a researcher at the Cersa (Centre d’Études et de Recherches de Sciences Administratives et Politiques) -CNRS, Université Paris 2-. She works on the application of natural language processing (NLP) to legal discourse and legal communication, and on the potentialities of Web 2.0 for participatory democracy.

VoxPopuLII is edited by Judith Pratt. Editor-in-Chief is Robert Richards, to whom queries should be directed. The statements above are not legal advice or legal representation. If you require legal advice, consult a lawyer. Find a lawyer in the Cornell LII Lawyer Directory.

In this post, I will describe how natural language processing can help in creating computer systems dealing with the law.

Law Books EmileA lot of computer systems are being designed to help users deal with legal texts — accessing, understanding, or applying them. [Editor's Note: Michael Poulshock's Jureeka is an example of a system that automates the application of legal texts.] Other systems — such as DALOS — are about creating legal texts, providing support for the writers, or simulating the effects of a text. Such systems are based on something more than “just” the legal text: there is XML mark-up, an OWL ontology, or a representation of the rules in SWRL or some programming language. This means that any piece of legislation that you want to use on your computer system needs to be translated into this computer representation.

We try to support this translation using natural language processing, so that (part of) the translation can be done by a computer. This automation should have a number of advantages. First of all, computers are cheaper than human experts, and automating the process should reduce the amount of resources needed for this task. Second, the models that are produced by automated processes are more consistent; human experts may treat two similar sentences differently, but a computer program will always behave the same. Finally, an approach employing structures ensures that there is a clear mapping between the elements of the computer model and the original text.

Natural Language Processing isn’t perfect yet: computers cannot understand human language. However, legal text is quite structured, and offers a lot more handholds for automated translation than, say, a novel.

Document Structure

The first step that we will have to undertake is to determine the structure of the document. Online services like Legislation.gov.uk and wetten.nl can make it easier to access legal documents because they can point you to the right part of the document (such as a chapter, paragraph, sentence, etc.). In most law texts, the structure has been made explicit using clear headings, like: Chapter 1 or Chapter 1. General Provisions. So, in order to detect structure, we need to detect these headings. This means we’ll need to search the document for lines starting with Chapter, followed by some designation (which we refer to as an index), and perhaps followed by some text – say, the title of the chapter. The index can Numbersbe a lot of things: Arabic numbers (1, 2, 3, …), Roman numbers (I, II, III, …) or letters (a, b, c, …). Sometimes the index is an ordinal appearing before the chapter label: First chapter. It may even be a combination of several numbers and letters (5.2a). This is not a great problem, as we can more or less assume that whatever follows the word Chapter is the index.

The main problem with this approach is that there are also regular sentences that start with the word Chapter, and we need to separate those out. To do so, we can use some heuristics: A title will not end with a full stop (.); a heading will always start on a new line; etc.

Queen BeatrixThis procedure to find the headings for chapters is repeated to find headings for sections, subsections, etc. Also, some sections (like numbered paragraphs or list items) will not have a full heading, but just a number, which we also need to recognise. Finally, some sections don’t have a heading but can be recognised because they start with a fixed language pattern. For example, a preamble in a (recent) Dutch Law — such as this — will start with: We, Beatrix, Queen of the Netherlands, Princess of Orange-Nassau, etc. etc. etc.

This procedure assumes that the input for the process is just text. Many documents will contain more information — such as textual markup — and headings may be more easily identified because they are marked as bold text, or even as headings. So, in situations where the input is made up of documents that are marked-up in a consistent way, it may be easier to recognise the patterns by taking layout into account in addition to text.

To actually find the patterns, we can use existing toolkits like GATE. After the patterns have been found, and the structure has been recognised, we can store it using a format such as MetaLex.

References

The second step is to detect the references from a portion of a law text to other portions of that text, or from a law text to other texts. References, like headings, follow a pattern. The simplest patterns are rather similar to headings; the text chapter 13 is probably a reference, unless it is part of a heading. ReferencesJust like headings, basic references consist of a label (section, chapter, article) and an index (13, 13.2.1, XIII,  m). And, just as with headings, we can find the references by looking for these patterns in the text.

However, this is only the simplest form of references. Besides references to a specific section, such as chapter 13, there are of course also references to a complete law. Some of these references follow a pattern as well, such as the law of October 1st, 2007. Most laws are cited by means of a citation title, though, such as the Railroad Act. Such titles can contain all kinds of words, and they don’t follow a strict pattern. Thus, such references cannot be detected using patterns. Instead, we use a list containing all (citation) titles to detect such references.

Other, more complex references contain multiple references in one statement, such as articles 13 and 14, or multiple levels: article 13, item e, of the Railroad Act, or even more complex combinations of the two: articles 13, item e, 14, item f, 15 and 16, items a and b, of the Railroad Act. Though more complex than the simple combination of label and index, these references still follow clear (sometimes recurring) patterns, and can be found in the text by searching for such patterns.

At the Leibniz Center for Law, we’ve created a parser based on these patterns, which had an accuracy of over 95%. For each reference found, we can construct some standardised name, and store it. With this technology, not only can we add hyperlinks to documents; we can also search for documents that refer to some specific document.

Classification

Now that we’ve got the structure and links in place, it’s time to start with the actual meaning of the text. Rather than tackling the entire text as a whole, we’ve selected sentences as the basic building blocks, and we attempt to create computer models for individual sentences first. Later, we can integrate those individual models to a complete model.

As a first step in creating the models, we start by assigning a broad meaning, or classification, to each sentence. Does the sentence give a definition for a concept, describe an obligation, or make a change in another law? In total, we distinguish fourteen different classes of sentences that appear in Dutch law texts. The next step in our automated approach is to assign a class to each sentence automatically.

To do so, we turn once again to language patterns. Legal language is rather strict, and legislative drafters don’t vary their language a lot — in a novel, variation may make for a more appealing text, but in a law, variation invites ambiguity. In fact, there are official Guidelines for Legislative Drafting that (among other things) reduce the variety of texts used. [Editor's Note: For example, drafters of legislation in the U.S. House of Representatives Office of the Legislative Counsel have used Donald Hirsch's Drafting Federal Law.] This means that for each of our classes, there’s a rather limited set of language patterns used. For example, definitions will look like one of these:

Under … is understood …

This law understands under … …

There are some variations in word order, but in the end, a small set of patterns is sufficient to describe all commonly used phrases. There is only one class of sentences where we cannot define a full set of patterns: obligations. In Dutch laws, obligations are often expressed without signal words like must or is obliged to. Instead, the obligations are presented as a fact:

No bodies are buried on a closed cemetery.

However, since the obligations are the only sentences lacking all-compassing patterns, we will assume that any sentence that does not mark a pattern is one of these obligations.

Based on the patterns found, we’ve created a classifier that attempts to sort sentences into these different classes. This classifier has an accuracy of 91%, and we expect that this can improved a bit further.

(As a side note: For classification tasks as these, a machine learning approach is often preferred; see, e.g., here. With such an approach, you provide the computer, not with patterns, but with a bunch of sample sentences. The computer will then extract its own patterns from those sentences, and use these to classify any new sentences. We’ve tried this approach as well (using the toolkit WEKA), and reached similarly accurate results.)

Modelling

Having classified the sentences, we now want to create models of the sentences. In essence, this means breaking down each sentence into smaller components and defining relationships between them. In some cases, the patterns used to classify the sentence already give us sufficient information to break up the sentence. Suppose we have a sentence like:

In article 7.12, sub one, second sentence, «article 7.3b» is replaced by: article 7.3c.

We classify this sentence as a replacement because of the text is replaced by. We can then also conclude that the text between angle quotes is the text to be replaced, the text following the colon is the replacing text, and the reference preceding it (which we’ve already detected) is the location where the replacement should take place.

This works fine for sentences that are somehow “about” the law. But for sentences that deal with some other domain, such as taxes, traffic, or commerce, we cannot predict all the elements. These sentences could be about anything — and statutes are full of such sentences. For such sentences, we need to follow a generic method. The aim is to model rules as a situation or action that is allowed or not allowed, similar to the models created in the HARNESS system of the ESTRELLA project. For example, for an obligation, we assume that the sentence describes some action that must be done. warrantWe try to identify who should be doing the action, and what other elements are involved. Thus, for the sentence:

Our Minister issues a warrant to the negligent person.

we would like to extract the following information:

Obligation
Action: Issue
Agent: Our Minister
Patient: Warrant
Recipient: Negligent person

(Such a table, or frame, is not the same as a computer model, but has all the elements needed to create one.)

Now, identifying these different elements of the sentence (agent, patient, recipient) is something that computer linguists have already worked on for a long time, which means we do not have to start from scratch. Instead, we can use existing parsers to do much of the work for us. For our Dutch laws, we use the Alpino parser. voxemiletree.jpgSuch a parser will create a parse tree of a sentence. In this parse tree, the sentence will be split up in parts. The parser can identify which part is the subject, the direct object, the indirect object, etc. Based on this information, we can determine the agent, patient, and recipient (so-called semantic roles). In a sentence with a verb in the active voice, the subject is the agent, the direct object is the patient, and the indirect object is the recipient. Furthermore, the parser will determine the relationship between words, such as an adjective that modifies a noun. This information, too, helps us to make more accurate models.

We start out with the output of these parsers, and then try to extract all terms that have some more significance. If we want an application to compute whether or not a situation is allowed, a word like car can be treated in a generic way, but terms like allowed and not some special attention.

To Be Continued…

We still need to refine the method for making these models, and evaluate the results. After that, the individual models will need to be merged. But even as things stand now, we think these tools will help with getting legal text from paper into your computer systems.

[Editor's Note: For more information about this topic, please see Dr. Adam Wyner's post, Weaving the Legal Semantic Web with Natural Language Processing.]

Emile_de_MaatEmile de Maat is a researcher at the Leibniz Center for Law (University of Amsterdam). His research focuses on the automatic extraction of metadata and meaning from legal sources.

VoxPopuLII is edited by Judith Pratt. Editor in chief is Robert Richards.

CornucopiaThe World Wide Web is a virtual cornucopia of legal information bearing on all manner of topics and in a spectrum of formats, much of it textual. However, to make use of this storehouse of textual information, it must be annotated and structured in such a way as to be meaningful to people and processable by computers. One of the visions of the Semantic Web has been to enrich information on the Web with annotation and structure. Yet, given that text is in a natural language (e.g., English, German, Japanese, etc.), which people can understand but machines cannot, some automated processing of the text itself is needed before further processing can be applied. In this article, we discuss one approach to legal information on the World Wide Web, the Semantic Web, and Natural Language Processing (NLP). Each of these are large, complex, and heterogeneous topics of research; in this short post, we can only hope to touch on a fragment and that heavily biased to our interests and knowledge. Other important approaches are mentioned at the end of the post. We give small working examples of legal textual input, the Semantic Web output, and how NLP can be used to process the input into the output.

Legal Information on the Web

For clients, legal professionals, and public administrators, the Web provides an unprecedented opportunity to search for, find, and reason with legal information such as case law, legislation, legal opinions, journal articles, and material relevant to discovery in a court procedure. With a search tool such as Google or indexed searches made available by Lexis-Nexis, Westlaw, or the World Legal Information Institute, the legal researcher can input key words into a search and get in return a (usually long) list of documents which contain, or are indexed by, those key words.

As useful as such searches are, they are also highly limited to the particular words or indexation provided, for the legal researcher must still manually examine the documents to find the substantive information. Moreover, current legal search mechanisms do not support more meaningful searches such as for properties or relationships, where, for example, a legal researcher searches for cases in which a company has the property of being in the role of plaintiff or where a lawyer is in the relationship of representing a client. Nor, by the same token, can searches be made with respect to more general (or more specific) concepts, such as “all cases in which a company has any role,” some particular fact pattern, legislation bearing on related topics, or decisions on topics related to a legal subject.

Binary MysteryThe underlying problem is that legal textual information is expressed in natural language. What literate people read as meaningful words and sentences appear to a computer as just strings of ones and zeros. Only by imposing some structure on the binary code is it converted to textual characters as we know them. Yet, there is no similar widespread system for converting the characters into higher levels of structure which correlate to our understanding of meaning. While a search can be made for the string plaintiff, there are no (widely available) searches for a string that represents an individual who bears the role of plaintiff. To make language on the Web more meaningful and structured, additional content must be added to the source material, which is where the Semantic Web and Natural Language Processing come into play.

Semantic Web

The Semantic Web is a complex of design principles and technologies which are intended to make information on the Web more meaningful and usable to people.Semantic Web Stack We focus on only a small portion of this structure, namely the syntactic XML (eXtensible Markup Language) level, where elements are annotated so as to indicate linguistically relevant information and structure. (Click here for more on these points.) While the XML level may be construed as a ‘lower’ level in the Semantic Web “stack” — i.e., the layers of interrelated technologies that make up the Semantic Web — the XML level is nonetheless crucial to providing information to higher levels where ontologies (and click here for more on this) and logic play a role. So as to be clear about the relation between the Semantic Web and NLP, we briefly review aspects of XML by example, and furnish motivations as we go.

Suppose one looks up a case where Harris Hill is the plaintiff and Jane Smith is the attorney for Harris Hill. In a document related to this case, we would see text such as the following portions:

Harris Hill, plaintiff.
Jane Smith, attorney for the plaintiff.

While it is relatively straightforward to structure the binary string into characters, adding further information is more difficult. Consider what we know about this small fragment: Harris and Jane are (very likely) first names, Hill and Smith are last names, Harris Hill and Jane Smith are full names of people, plaintiff and attorney are roles in a legal case, Harris Hill has the role of plaintiff, attorney for is a relationship between two entities, and Jane Smith is in the attorney for relationship to Harris Hill. It would be useful to encode this information into a standardised machine-readable and processable form.

XML helps to encode the information by specifying requirements for tags that can be used to annotate the text. It is a highly expressive language, allowing one to define tags that suit one’s purposes so long as the specification requirements are met. One requirement is that each tag has a beginning and an ending; the material in between is the data that is being tagged. For example, suppose tags such as the following, where … indicates the data:


<legalcase>...</legalcase>,
<firstname>...</firstname>,
<lastname>...</lastname>,
<fullname>...</fullname>,
<plaintiff>...</plaintiff>,
<attorney>...</attorney>, 
<legalrelationship>...</legalrelationship>

Another requirement is that the tags have a tree structure, where each pair of tags in the document is included in another pair of tags and there is no crossing over:


<fullname><firstname>...</firstname>, 
<lastname>...</lastname></fullname>

is acceptable, but


<fullname><firstname>...<lastname>
</firstname> ...</lastname></fullname>

is unacceptable. Finally, XML tags can be organised into schemas to structure the tags.

With these points in mind, we could represent our fragment as:


<legalcase>
  <legalrelationship>
    <plaintiff>
      <fullname><firstname>Harris</firstname>,
           <lastname>Hill</lastname></fullname>
    </plaintiff>,
    <attorney>
      <fullname><firstname>Jane</firstname>,
           <lastname>Smith</lastname></fullname>
    </attorney>
  </legalrelationship
</legalcase>

We have added structured information — the tags — to the original text. While this is more difficult for us to read, it is very easy for a machine to read and process. In addition, the tagged text contains the content of the information, which can be presented in a range of alternative ways and formats using a transformation language such as XSLT (click here for more on this point) so that we have an easier-to-read format.

Why bother to include all this additional information in a legal text? Because these additions allow us to query the source text and submit the information to further processing such as inference. Given a query language, we could submit to the machine the query Who is the attorney in the case? and the answer would be Jane Smith. Given a rule language — such as RuleML or Semantic Web Rule Language (SWRL) — which has a rule such as If someone is an attorney for a client then that client has a privileged relationship with the attorney, it might follow from this rule that the attorney could not divulge the client’s secrets. Applying such a rule to our sample, we could infer that Jane Smith cannot divulge Harris Hill’s secrets.

Tower of BabelThough it may seem here like too much technology for such a small and obvious task, it is essential where we scale up our queries and inferences on large corpora of legal texts — hundreds of thousands if not millions of documents — which comprise vast storehouses of unstructured, yet meaningful data. Were all legal cases uniformly annotated, we could, in principle, find out every attorney for every plaintiff for every legal case. Where our tagging structure is very rich, our queries and inferences could also be very rich and detailed. Perhaps a more familiar way to view documents annotated with XML is as a database to which further processes can be applied over the Web.

Natural Language Processing

As we have presented it, we have an input, the corpus of texts, and an output, texts annotated with XML tags. The objective is to support a range of processes such as querying and inference. However, getting from a corpus of textual information to annotated output is a demanding task, generically referred to as the knowledge acquisition bottleneck. Not only is the task demanding on resources (time, money, manpower); it is also highly knowledge intensive since whoever is doing the annotation must know what to look for, and it is important that all of the annotators annotate the text in the same way (inter-annotator agreement) to support the processes. Thus, automation is central.

Yet processing language to support such richly annotated documents confronts a spectrum of difficult issues. Among them, natural language supports (1) implicit or presupposed information, (2) multiple forms with the same meaning, (3) the same form with different contextually dependent meanings, and (4) dispersed meanings. (Similar points can be made for sentences or other linguistic elements.) Here are examples of these four issues:

(1) “When did you stop taking drugs?” (presupposes that the person being questioned took drugs at sometime in the past);
(2) Jane Smith, Jane R. Smith, Smith, Attorney Smith… (different ways to refer to the same person);
(3) The individual referred to by the name “Jane Smith” in one case decision may not be the individual referred to by the name “Jane Smith” in another case decision;
(4) Jane Smith represented Jones Inc. She works for Dewey, Cheetum, and Howe. To contact her, write to j.smith@dch.com .

When we search for information, a range of linguistic structures or relationships may be relevant to our query, such as:

People grasp relationships between words and phrases, such that Bill exercises daily contrasts with the meaning of Bill is a couch potato, or that if it is true that Bill used a knife to kill Phil, then Bill killed Phil. Finally, meaning tends to be sparse; that is, there are a few words and patterns that occur very regularly, while most words or patterns occur relatively rarely in the corpus.

Natural language processing (NLP) takes on this highly complex and daunting problem as an engineering problem, decomposing large problems into smaller problems and subdomains until it gets to those which it can begin to address. Having found a solution to smaller problems, NLP can then address other problems or larger scope problems. Some of the subtopics in NLP are:

  • Generation – converting information in a database into natural language.
  • Understanding – converting natural language into a machine-readable form.
  • Information Retrieval – gathering documents which contain key words or phrases. This is essentially what is done by Google.
  • Text Summarization – summarizing (in a paragraph) the main meaning of a text or corpus.
  • Question Answering – making queries and giving answers to them, in natural language, with respect to some corpus of texts.
  • Information Extraction — identifying, annotating, and extracting information from documents for reuse, representation, or reasoning.

In this article, we are primarily (here) interested in information extraction.

NLP Approaches: Knowledge Light v. Knowledge Heavy

There are a range of techniques that one can apply to analyse the linguistic data obtained from legal texts; each of these techniques has strengths and weaknesses with respect to different problems. Statistical and machine-learning techniques are considered “knowledge light.” With statistical approaches, the processing presumes very little knowledge by the system (or analyst). Rather, algorithms are applied that compare and contrast large bodies of textual data, and identify regularities and similarities. Such algorithms encounter problems with sparse data or patterns that are widely dispersed across the text. (See Turney and Pantel (2010) for an overview of this area.) Machine learning approaches apply learning algorithms to annotated material to extend results to unannotated material, thus introducing more knowledge into the processing pipeline. However, the results are somewhat of a black box in that we cannot really know the rules that are learned and use them further.

With a “knowledge-heavy” approach, we know, in a sense, what we are looking for, and make this knowledge explicit in lists and rules for processing. Yet, this is labour- and knowledge-intensive. In the legal domain it is crucial to have humanly understandable explanations and justifications for the analysis of a text, which to our thinking warrants a knowledge-heavy approach.

One open source text-mining package, General Architecture for Text Engineering (GATE), consists of multiple components in a cascade or pipeline, each component automatically processing some aspect of the text, and then feeding into the next process. The underlying strategy in all the components is to find a pattern (from either a list or a previous process) which matches a rule, and then to apply the rule which annotates the text. Each component performs a particular process on the text, such as:

  • Sentence segmentation – dividing text into sentences.
  • Tokenisation – words identified by spaces between them.
  • Part-of-speech tagging – noun, verb, adjective, etc., determined by look-up and relationships among words.
  • Shallow syntactic parsing/chunking – dividing the text by noun phrase, verb phrase, subordinate clause, etc.
  • Named entity recognition – the entities in the text such as organisations, people, and places.
  • Dependency analysis – subordinate clauses, pronominal anaphora [i.e., identifying what a pronoun refers to], etc.

The system can also be used to annotate more specifically to elements of interest. In one study, we annotated legal cases from a case base (a corpus of cases) in order to identify a range of particular pieces of information that would be relevant to legal professionals such as:

  • Case citation.
  • Names of parties.
  • Roles of parties, meaning plaintiff or defendant.
  • Type of court.
  • Names of judges.
  • Names of attorneys.
  • Roles of attorneys, meaning the side they represent.
  • Final decision.
  • Cases cited.
  • Nature of the case, meaning using keywords to classify the case in terms of subject (e.g., criminal assault, intellectual property, etc.)

Applying our lists and rules to a corpus of legal cases, a sample output is as follows, where the coloured highlights are annotated as per the key on the right; the colours are a visualisation of the sorts of tags discussed above (to see a larger version of the image, right click on the image, then click on “View Image” or a similar phrase; when finished viewing the image, use the browser’s back button to return to the text):

Annotation of a Criminal Case

The approach is very flexible and appears in similar systems. (See, for example, de Maat and Winkels, Automatic Classification of Sentences in Dutch Laws (2008).) While it is labour intensive to develop and maintain such list and rule systems, with a collaborative, Web-based approach, it may be feasible to construct rich systems to annotate large domains.

Conclusion

In this post, we have given a very brief overview of how the Semantic Web and Natural Language Processing (NLP) apply to legal textual information to support annotation which then enables querying and inference. Of course, this is but one take on a much larger domain. In our view, it holds great promise in making legal information more transparent and available to more legal professionals. Aside from GATE, some other resources on text analytics and NLP are textbooks and lecture notes (see, e.g., Wilcock), as well as workshops (such as SPLeT and LOAIT). While applications of Natural Language Processing to legal materials are largely lab studies, the use of NLP in conjunction with Semantic Web technology to annotate legal texts is a fast-developing, results-oriented area which targets meaningful applications for legal professionals. It is well worth watching.

Adam WynerDr. Adam Zachary Wyner is a Research Fellow at the University of Leeds, Institute of Communication Studies, Centre for Digital Citizenship. He currently works on the EU-funded project IMPACT: Integrated Method for Policy Making Using Argument Modelling and Computer Assisted Text Analysis. Dr. Wyner has a Ph.D. in Linguistics (Cornell, 1994) and a Ph.D. in Computer Science (King’s College London, 2008). His computer science Ph.D. dissertation is entitled Violations and Fulfillments in the Formal Representation of Contracts. He has published in the syntax and semantics of adverbs, deontic logic, legal ontologies, and argumentation theory with special reference to law. He is workshop co-chair of SPLeT 2010: Workshop on Semantic Processing of Legal Texts, to be held 23 May 2010 in Malta. He writes about his research at his blog, Language, Logic, Law, Software.

VoxPopuLII is edited by Judith Pratt. Editor in chief is Robert Richards.