Logic Language Law Computing – Page 10 – Adam Zachary Wyner, Associate Professor of Law and Computer Science, Swansea University

Introduction to a Series of Posts on Legal Information Extraction with GATE

This post has notes on and links to several other posts about legal information annotation and extraction using the General Architecture for Text Engineering system (GATE). The information in the posts was presented at my tutorial at JURIX 2009, Rotterdam, The Netherlands; the slides are available here. See the GATE website or my slides for introductory material about NLP and text annotation. For particulars about NLP and legal resources, see the posts and files at the links below.
The Posts
The following posts discuss different aspects of legal information extraction using GATE (live links indicate live posts):

Legislative rule extraction: annotates (non-conditional) rules from legislation.
Using XSLT to re-represent GATE output: uses the information extracted from legislation and reformats it using XSLT.
Information extraction of conditional rules: illustrates using GATE to annotate conditional statements of the form If P, then Q, which are common in law.
Information extraction of legal case factors: shows how to annotate factors of a case, which are prototypical fact patterns and crucial to case based reasoning.
Information extraction of legal case features using lists and rules: illustrates annotation of case features, which include a range of information about a legal case such as jurisdiction, parties, decision, and others; the post provides lists and rules to annotate this information.
Information extraction of legal case features using an ontology: shows how to use an ontology (rather than lists and rules) to annotate text.
Information extraction with ANNIC: uses a GATE plugin to analyse and query annotations such as case factors.

Prototypes
The samples presented in the posts are prototypes only. No doubt there are other ways to accomplish similar tasks, the material is not as streamlined or cleanly presented as it could be, and each section is but a very small fragment of a much larger problem. In addition, there are better ways to present the lists and rules “in one piece”; however, during development and for discussion, it seems more helpful to have elements separate. Nonetheless, as a proof of concept, the samples make their point.
If there are any problems, contact Adam Wyner at adam@wyner.info.
Files
The posts are intended to be self-contained and to work with GATE 5.0. The archive files include the .xgapp file, which is a saved application state, along with text/corpus, the lists, and JAPE rules needed to run the application. In addition, the archive files include any graph outputs as reference. As noted, one may need to ‘fiddle’ a bit with the gazetteer lists in the current version.
Graphics
Graphics in the posts can be viewed in a larger and clearer size by right clicking on the graphic and selecting View Image. The Back button on your browser will close the image and return you to the post.
License
The materials are released under the following license:
By Adam Wyner
Distributed under the Creative Commons
Attribution-Non-Commercial-Share Alike 2.0
If you want to commercially exploit the material, you must seek a separate license with me. That said, I look forward to further open development on these materials; see my post on Open Source Legal Information.

Using XSLT to Re-represent GATE Output

Once one has processed some documents with GATE, what can one do with the result? After all, information extraction implies that the information is extracted, not simply annotated. (See introductory notes on this and related posts.)
There are several paths. One is to use Annotations in Context (ANNIC), which searches for and returns a display of annotated elements; we discuss how to use ANNIC in a separate post. However, this does not appear to support an “export” function to further process the results. Another path is to export the document with inline annotations; this, with a bit of further manual work, can then be processed further with EXtensible Stylesheet Language Transformations — XSLT. There are other approaches (e.g. XQUERY), but this post provides an example of using XSLT to present output as a rule book.
In Legislative Rule Extraction, we annotated some legislation. We carry on with the annotated legislation.
Output of GATE
In addition to the graphic output from GATE’s application, we can output the results of the annotation either inline or offset. As we are interested to provide alternative presentations of the annotated material, we look at the inline annotation.
In GATE, by right clicking on the document file (after applying the application to it) and choose “Save preserving document format'”. For out sample text, the result is:


 Article 1 
 Subject matter 
 This Directive lays down rules concerning the
following :
 1) 
 the taking-up and pursuit, within the Community,
of the self-employed activities of direct insurance and
reinsurance ;
 2) 
 the supervision in the case of insurance and
reinsurance groups ;
 3) 
 the reorganisation and winding-up of direct
insurance undertakings .

Legal XML
The GATE output needs to be made into proper XML, having a root and being properly nested. As there will be several rules, each rule extracted should go between some legal XML annotation. There is an issue about how to save and process a full corpus, as the only options to save are XML or Datastore, but we leave this aside for the time being. For now, we ‘manually’ wrap our GATE output as below.
I used the online XSLT editor at w3schools, which has nice online functionality which allows one to experiment and get results right away. In particular, one can cut and paste the XML rulebook (below) into the left hand pane and the XSLT code (below) into the right hand pane, hit the edit button, and get the transformed output. Caveat, one might have to do a bit of editing on the XML rulebook for spaces and returns since there are some bumps between what appears in WordPress and what is needed to run code.
The XML Rulebook:





 Article 1 
 Subject matter 
 This Directive lays down rules concerning the
following :
 1) 
 the taking-up and pursuit, within the Community,
of the self-employed activities of direct insurance and
reinsurance ;
 2) 
 the supervision in the case of insurance and
reinsurance groups ;
 3) 
 the reorganisation and winding-up of direct
insurance undertakings .

The XSLT code:






  
  
  My Rulebook
  
  
  


  
    
    
    
    
    
  


  Reference Code: 
  
  



  Title: 
  
  



  Description: 
  
  



  Description: 
  
  



  Description:

XSLT Output
The result is the following:
Output of XSLT on the XML Rulebook
In general, one can create any number of rulebooks from the same underlying data, varying the layout and substance of the presentation. For example, we can change the colours or headers easily; we can present more or less information. This is a lot more powerful than the static book that now exists.
Problems and Issues
Our example is a simple illustration of what can be done. Note that we have not yet fulfilled the requirements from our initial post since we have not numbered the sections, but this can be added later.
An important problem is that GATE annotations are not always in accordance with XML standards. In particular, XML markups must be strictly embedded as in

There can be no crossover such as in

though this may well occur for GATE annotations. There may be several approaches to this problem, but we leave that for future discussion.
Another problem is that “Save preserving document format” only works with documents and not corpora, and we might want to work with corpora.
Finally, XSLT is useful for transforming XSL files, not in extracting information from XML files, for which one would need something such as XQuery.
By Adam Wyner
Distributed under the Creative Commons
Attribution-Non-Commercial-Share Alike 2.0

Legislative Rule Extraction

In this post, we discuss the annotation of information from legislation, for example, to create a rule book from legislation. There are two distinct tasks and two tools. First, we want to take the original legislation and annotate it; for this, we use GATE. Second, we want to transform the output of GATE, using the annotations, into some alternative, web-compatible format; for this, we use EXtensible Stylesheet Language Transformations (XSLT). This is presented in STUB. John Cyriac of compliancetrack outlined the problem that is addressed in these two posts. (See introductory notes on this and related posts.)
Sample legislation and text
The text we are working with is a sample from Insurance and Reinsurance (Solvency II) from the European Parliament.
SUBJECT MATTER AND SCOPE
Article 1
Subject matter
This Directive lays down rules concerning the following:
1) the taking-up and pursuit, within the Community, of the self-employed activities of direct insurance and insurance;
2) the supervision in the case of insurance and reinsurance groups;
3) the reorganisation and winding-up of direct insurance undertakings.
Article 2
Scope
1. This Directive shall apply to direct life and non-life insurance undertakings which are established in the territory of a Member State or which wish to become established there. It shall also apply to reinsurance undertakings, which conduct only reinsurance activities, and which are established in the territory of a Member State or which wish to become established there with the exception of Title IV.
There are additional articles which we do not work with. The article is not a logical statement (an If, then statement), but identifies the matters which the directive is concerned with. Each statement of the article may be understood as a conjunct: the rules concern a, b, and c. However, this is not yet relevant to our analysis. See the separate post about rule extraction for conditionals.
Target result
We want to annotate the first article, picking out each section for extraction. In particular, for a practitioner to use the extraction, he should have it in a format which identifies the following:
Reference Code: Article 1
Title: Subject Matter
Level: 1.0
Description: This Directive lays down rules concerning the following:
Level: 1.1
Description: the taking-up and pursuit, within the Community, of the self-employed activities of direct insurance and reinsurance;
Level: 1.2
Description: the supervision in the case of insurance and reinsurance groups;
Level: 1.3
Description: the reorganisation and winding-up of direct insurance undertakings;
Output
The output of GATE appears in the following figure:
Annotating the structure of legislative rules
GATE
To get this output, we used the files and application state in GATELegislativeRulebook.tar.gz.
Text
The text is a fragment of the legislation above and is found in the SmallRulebookText.tex file.
Lists
We use the following lists in addition to standard ANNIE lists, meaning that a lists.def file ought to incorporate the files. This is the resource ListGaz given in the .xgapp file (though this may require some additional fiddling and files to work).

roman_numerals_i-xx.lst: It has majorType = roman_numeral. This is a list of roman numbers from i to xx.
rulebooksectionlabel.lst: It has majorType = rulebooksection. This is a list of section headings such as: Subject matter, Scope, Statutory systems, Exclusion from scope due to size, Operations, Assistance, Mutual undertakings, Institutions, Operations and activities.

The list of section headings is taken from the legislation, which presumably follows standard guidelines for section heading labels. For the list of roman numerals, there are more general methods using Regex to match well-formed numerals (see Roman Numerals in Python and Regex for Roman Numerals); however, for our purposes it is simpler to use limited lists rather than Regex. In either case, several problems arise, as we see later.
JAPE rules

ListArticleSection.jape: What is annotated with Article (from the lookup) and a number is annotated ArticleFlag.
ListFlagLevel1.jape: The string number followed by a period of closed parenthesis is annotated ListFlagLevel1.
ListFlagLevel1sub.jape: A number followed by a letter followed by a period is annotated ListFlagLevel1sub.
ListFlagLevel2.jape: A string of lower case letters followed by a closed parenthesis is annotated ListFlagLevel2.
ListFlagLevel3.jape: A roman number from a lookup list followed by a closed parenthsis is annotated ListFlagLevel3.
RuleBookSectionLabel.jape: Looks up section labels from a list and annotates them SectionType. For example, Subject matter, Scope, and Statutory systems.
ListStatement01.jape: A string which occurs between SectionType annotation and a colon is annotated ListStateTop.
ListStatement02.jape: A string which occurs between a ListFlagLevel1 and a semicolon is annotated SubListStatementPrefinal.
ListStatement03.jape: A string which occurs between a ListFlagLevel1 and a period is annotated SubListStatementFinal.

Application order
The order of application of the processing resources is:

Document Reset PR
ANNIE Sentence Splitter
ANNIE English Tokeniser
ListGaz
RulebookSectionLabel:
ListArticleSection
ListStatement01
ListFlagLevel01
ListStatement02
ListStatement03

Additional issues
This example does not show the other list flag levels (e.g. using letters, roman numerals etc.), nor the results on other parts of the legislation.
While the result for the specific text is attractive, there is much work to be done. The lists and rules overgenerate. For example, the rules indicate that avrt is a level flag because v is recognised as a roman numeral. In other cases, too long a passage is selected as the statement at the top of the list. Yet, the example is still useful to demonstrate a proof of concept, particularly in conjunction with the post on XSLT.
By Adam Wyner
Distributed under the Creative Commons
Attribution-Non-Commercial-Share Alike 2.0

Discussion with Jeremy Tobias-Tarsh of Practical Law Company

On Wednesday January 13, 2010, I had a meeting with Jeremy Tobias-Tarsh, director of Practical Law Company (PLC) and currently in charge of overseeing the company’s three year development plan. We had a very engaging, far-ranging discussion about the company’s interests in technological innovation in the legal domain. His colleagues at the meeting where Brigitte Kaltenbacher, who works on usability tests for searches among the company’s resources, and Sara Stangalini, who works with Brigitte.
The post gives an overview of our discussion — what PLC does, the ambitions for the future, a range of issues and tools to handle them, and some suggestions about moving ahead.
About PLC
PLC provides know-how for lawyers, meaning written analysis of current legal developments, practice notes (legal situations lawyers face and how the law treats them), standard draft documents, and checklists for managing actions. The services cover a range of legal areas such as arbitration, competition, corporate, construction, employment, finance, pensions, tax, and so on.
Jeremy spoke of an ambition at the company to use Semantic Web technologies on the company’s resources in order to give users faster, more precise, more meaningful and relevant results for searches in the resources — making the company’s content more findable. This might be done by annotating the content of the resources and supporting search with respect to the annotations. (Along these lines, an important advantage is that the company has been using an XML editor (Epic) for its documents for some time, so there is broad and widespread familiarity with what XML offers.)
Similarly, PLC could develop tools which improve the searches among a law firm’s documents. This is especially crucial where searches are done by junior staff with less knowledge of how and where to search. As made clear in discussions of knowledge management in law firms, an important task of senior lawyers in a firm is to train the new and junior lawyers in the details of the practice. While law schools may train law students in legal analysis and the law, the students may be unprepared for how to practice, which may have less to do with the law and more to do with finding and working with the relevant documents.
Any technology which can support junior lawyers in learning their tasks would be an advantage. In addition, any technology which could encode a senior lawyer’s knowledge would be useful to share throughout the firm and to preserve that knowledge where the lawyer is unavailable.
Some Sample Problems and Tools
Contracts
An instance of such a tool might apply to contracts. PLC and firms have catalogues of preformatted draft documents, each of which may have variants developed over time. This may be seen as a contract base. A junior lawyer may be asked to find among this contract base a contract which is either an exact match for the current circumstances or close enough so that with some modifications it would suit. This can be viewed as an instance of case based reasoning, where the ‘factors’ are the particulars of the contracts and the current contractual setting. So, not only must there be some way to match similarity and difference among the documents, but there ought also to be some systematic way to manage the modifications.
To address this, three technologies could be used. Contracts could be annotated with the factors, then we apply case based reasoning. Alternatively, contracts could be linked to an ontology, so that the properties and relationships among the documents are made explicit. Researchers could search for the relevant documents using the ontology. Along with this, a contract modification tracking system, such as a modified version of which meets the MetaLex standard, could be developed.
Due Diligence
Another problem relates to due diligence. Law firms are up against constraints in terms of time and money in satisfying the requirements of due diligence. Firms increasingly are responsible to show due diligence in a wider range of areas. This means that more lawyers must be hired and more billable hours accrued. However, the companies hired by the law firms are reluctant to pay more for due diligence. Consequently, firms have a motivation to find ways to make due diligence more efficient. Moreover, it is not a task that junior lawyers can easily undertake without extensive training. Natural language expert systems might provide a useful technology.
Policy Consultations
We also had a discussion about policy consultations. PLC helped formed and serves as secretariat for the General Counsel 100 Group, which is comprised of senior legal officers drawn from FTSE 100 companies. The group is a forum for businesses to give input on policy consultations and to share best practices in law, risk management, compliance, and other common interests (see the various public papers on the link). In my EU Framework 7 proposal on argumentation, we explicitly referred to policy consultation as a key area to develop and apply the tool. Broadly speaking, we had a systematic plan to develop a tool which takes as input statements in natural language, then translates them into a logical formalism. Claims pro and con on a particular issue are systematically structured into an ‘argument’ network in order to ‘prove’ outcomes given premises as well as to provide sets of consistent statements for and against a claim. Other argument mapping technologies might be useful here as well.
Ontologies
We also talked about the development of ontologies and whether they can be automatically extracted from textual sources. This is an area where there is a lot of current interest and some significant progress.
Moving Ahead
Finally, we also touched on how to move ahead. A brainstorming and road-mapping exercisea could be very valuable experience. The exercise would include not only company representatives, but also clients served by PLC. Parties on ‘both sides of the fence’ could discover more about what they know, want, and imagine could be done. In addition, Jeremy suggested that I might be engaged to present some of the ‘main points’ about Semantic Web technologies and the law to some of PLC’s editors and clients.
It was an enjoyable and spirited discussion, which I hope we will find the opportunity in the near future to continue.
By Adam Wyner
Distributed under the Creative Commons
Attribution-Non-Commercial-Share Alike 2.0

Legal Informatics Start-up from Stanford University

The Stanford Daily, an online newspaper with news from Stanford University, reports the creation of a spin-off, start-up company Lex Machina which is the result of collaboration between the Law School and Department of Computer Science at Stanford. The focus of the company is to make intellectual property litigation more transparent; it covers patent infringement, copyright, trademark, antitrust, and certain trade secret lawsuits. There are commercial and non-commercial services.
This is an interesting development, particularly in terms of the collaboration between a law school and department of computer science. I hope it is the first of many, and I look forward to learning more about the company and system.

CFP: Workshop on Semantic Processing of Legal Texts

LREC 2010 Workshop on
SEMANTIC PROCESSING OF LEGAL TEXTS (SPLeT-2010)
CALL FOR PAPERS
23 May 2010, Malta
Workshop description
The legal domain represents a primary candidate for web-based information distribution, exchange and management, as testified by the numerous e-government, e-justice and e-democracy initiatives worldwide. The last few years have seen a growing body of research and practice in the field of Artificial Intelligence and Law which addresses a range of topics: automated legal reasoning and argumentation, semantic and cross-language legal information retrieval, document classification, legal drafting, legal knowledge discovery and extraction, as well as the construction of legal ontologies and their application to the law domain. In this context, it is of paramount importance to use Natural Language Processing techniques and tools that automate and facilitate the process of knowledge extraction from legal texts.
With the last two years, a number of dedicated workshops and tutorials specifically focussing on different aspects of semantic processing of legal texts has demonstrated the current interest in research on Artificial Intelligence and Law in combination with Language Resources (LR) and Human Langugage Technologies (HLT). The LREC 2008 Workshop on “Semantic processing of legal texts” was held in Marrakech, Morocco, on the 27th of May 2008. The JURIX 2008 Workshop on “the Natural Language Engineering of Legal Argumentation: Language, Logic, and Computation (NaLEA)”, which focussed on recent advances in natural language engineering and legal argumentation. The ICAIL 2009 Workshops “LOAIT ’09 – the 3rd Workshop on Legal Ontologies and Artificial Intelligence Techniques joint with the 2nd Workshop on Semantic Processing of Legal Texts” and “NALEA’09 – Workshop on the Natural Language Engineering of Legal Argumentation: Language, Logic, and Computation”, the former focussing on Legal Knowledge Representation with particular emphasis on the issue of ontology acquisition from legal texts, the latter tackling issues related to legal argumentation and linguistic technologies.
To continue this momentum, a 3rd Workshop on “Semantic Processing of Legal Texts” is being organised at the Language Resources and Evaluation Conference to bring to the attention of the broader LR/HLT community the specific technical challenges posed by the semantic processing of legal texts and also share with the community the motivations and objectives which make it of interest to researchers in legal informatics. The outcome of these interactions are expected to advance research and applications and foster interdisciplinary collaboration within the legal domain.
The main goals of the workshop are to provide an overview of the state-of-the-art in legal knowledge extraction and management, to explore new research and development directions and emerging trends, and to exchange information regarding legal LRs and HLTs and their applications.
Areas of Interest
The workshop will focus on the topics of the automatic extraction of information from legal texts and the structural organisation of the extracted knowledge. Particular emphasis will be given to the crucial role of language resources and human language technologies.
Papers are invited on, but not limited to, the following topics:

Building legal resources: terminologies, ontologies, corpora
Ontologies of legal texts, including subareas such as ontology acquisition, ontology customisation, ontology merging, ontology extension, ontology evolution, lexical information, etc.
Information retrieval and extraction from legal texts
Semantic annotation of legal texts
Legal text processing
Multilingual aspects of legal text semantic processing
Legal thesauri mapping
Automatic Classification of legal documents
Logical analysis of legal language
Automated parsing and translation of natural language arguments into a logical formalism
Linguistically-orientied XML mark up of legal arguments
Dialogue protocols for argumentation
Legal argument ontology
Computational theories of argumentation that are suitable to natural language
Controlled language systems for law.

Submissions
Submissions are solicited from researchers working on all aspects of semantic processing of legal texts. Authors are invited to submit papers describing original completed work, work in progress, interesting problems, case studies or research trends related to one or more of the topics of interest listed above. The final version of the accepted papers will be published in the Workshop Proceedings.
Short or full papers can be submitted. Short papers are expected to present new ideas or new visions that may influence the direction of future research, yet they may be less mature than full papers. While an exhaustive evaluation of the proposed ideas is not necessary, insight and in-depth understanding of the issues is expected. Full papers should be more well developed and evaluated. Short papers will be reviewed the same way as full papers by the Program Committee and will be published in the Workshop Proceedings.
Full paper submissions should not exceed 10 pages, short papers 6 pages; both should be typeset using a font size of 11 points. Style files will be made available by LREC for the camera-ready versions of accepted papers. Papers should be submitted electronically, no later than February 10, 2010. The only accepted format for submitted papers is Adobe PDF. Submission will be electronic using START paper submission software available at
SPLeT 2010 Workshop
Note that when submitting a paper through the START page, authors will be kindly asked to provide relevant information about the resources that have been used for the work described in their paper or that are the outcome of their research. In this way, authors will contribute to the LREC2010 Map, our new feature for LREC 2010. For further information on this initiative, please refer to
LREC2010 Map of Language Resources
Important Dates
Paper submission deadline: 10 February 2010
Acceptance notification sent: 5 March 2010
Final version deadline: 21 March 2010
Workshop date: 23 May 2010
Workshop Chairs

Enrico Francesconi (Istituto di Teoria e Tecniche dell’Informazione Giuridica of CNR, Florence, Italy)
Simonetta Montemagni (Istituto di Linguistica Computazionale of CNR, Pisa, Italy)
Wim Peters (Natural Language Processing Research Group, University of Sheffield, UK)
Adam Wyner (Department of Computer Science, University College London, UK)

Address any queries regarding the workshop to: lrec10_legalWS@ilc.cnr.it
Program Committee

Johan Bos (University of Rome, Italy)
Danièle Bourcier (Humboldt Universität, Berlin, Germany)
Thomas R. Bruce (Cornell Law School, Ithaca, NY, USA)
Pompeu Casanovas (Institut de Dret i Tecnologia, UAB, Barcelona, Spain)
Alessandro Lenci (Dipartimento di Linguistica, Università di Pisa, Pisa, Italy)
Leonardo Lesmo (Dipartimento di Informatica, Università di Torino, Torino, Italy)
Raquel Mochales Palau (Catholic University of Leuven, Belgium)
Paulo Quaresma (Universidade de Évora, Portugal)
Erich Schweighofer (Universität Wien, Rechtswissenschaftliche Fakultät, Wien, Austria)
Manfred Stede (University of Potsdam, Germany)
Daniela Tiscornia (Istituto di Teoria e Tecniche dell’Informazione Giuridica of CNR, Florence, Italy)
Tom van Engers (Leibniz Center for Law, University of Amsterdam, Netherlands)
Radboud Winkels (Leibniz Center for Law, University of Amsterdam, Netherlands)

Open Source Information Extraction: Data, Lists, Rules, and Development Environment

Open source software development and standards are widely discussed and practiced. It has led to a range of useful applications and services. GATE is one such example.
However, one quickly learns that open source can easily mean open to a certain extent: GATE is open source, but the applications and additional functionalities that are developed with respect to GATE often are not. On the one hand, this makes perfect sense as the applications and functionalities are added value, labour intensive, and so on. On the other hand, the scientific community cannot verify, validate, or build on prior work unless the applications and functionalities are available. This can also hinder commercial development since closed development impedes progress, dissemination, and a common framework from which everyone benefits. It also does not recognise the fundamentally experimental aspect of information extraction. In contrast, the rapid growth and contributions of the natural (Biology, Physics, Chemistry, etc) or theoretical (Maths) sciences could only have occurred in an open, transparent development environment.
I advocate open source information extraction where an information extraction result can only be reported if it can be independently verified and built on by members of the scientific community. This means that the following must be made available concurrent with the report of the result:

Data and corpora
Lists (e.g. gazetteers)
Rules (e.g. JAPE rules)
Any additional processing components (e.g. information extraction to schemes or XSLT)
Development environment (e.g. GATE)

In other words, the results must be independently reproducible in full. The slogan is:

No publication without replicability.

This would:

Contribute to the research community and build on past developments.
Support teaching and learning.
Encourage interchange. The Semantic Web chokes on different formats.
Return academic research to the common (i.e. largely taxpayer funded) good rather than owned by the researcher or university. If someone needs to keep their work private, they should work at a company.
Lead to distributive, collaborative research and results, reducing redundancy and increasing the scale and complexity of systems.

Solving the knowledge bottleneck, particularly in relation to language, has not and likely will not be solved by any one individual or research team. Open source information extraction will, I believe, make greater progress toward addressing it.
Obviously, money must be made somewhere. One source is public funding, including contributions from private organisations which see a value in building public infrastructure. Another source is, like other open source software, systems, or other public information, to make money “around” the free material by adding non-core goods, services, or advertising.
By Adam Wyner
Distributed under the Creative Commons
Attribution-Non-Commercial-Share Alike 2.0

Research on Argumentation at the Leibniz Center for Law in Amsterdam

I have a 3 month research job at the Leibniz Center for Law, University of Amsterdam starting February 1 and working with Tom van Engers. This is part of the IMPACT project:

IMPACT is an international project, partially funded by the European Commission under the 7th framework programme. It will conduct original research to develop and integrate formal, computational models of policy and arguments about policy, to facilitate deliberations about policy at a conceptual, language-independent level. To support the analysis of policy proposals in an inclusive way which respects the interests of all stakeholders, research on tools for reconstructing arguments from data resources distributed throughout the Internet will be conducted. The key problem is translation from these sources in natural language to formal argumentation structures, which will be input for automatic reasoning.

My role will be to set up a Ph.D. research project concerning the key problem. This is based on an unsuccessful larger research proposal that I made with Tom. I’ll be organising the database, the literature, some of the software, and outlining the approach the student would take. I’ll make notes on the progress as it happens.
I’m looking forward to living for a while in Amsterdam, working with Tom and my other colleagues at the center — Joost Breuker, Rinke Hoekstra, Emile de Maat. The Netherlands also has a very lively Department of Argumentation Theory. As an added bonus, my colleagues from Linguistics, Susan Rothstein and Fred Landman, are in Amsterdam on sabbatical. Will be a very interesting and fun period.

Natural Language Processing Techniques for Managing Legal Resources on the Semantic Web — Tutorial Slides

I gave a tutorial on natural language processing for legal resource management at the International Conference on Legal Information Systems (JURIX) 2009 in Rotterdam, The Netherlands. The slides are available below. Comments welcome.
The following people attended:

Andras Forhecz, Budapest University of Technology and Economics, Hungary
Ales Gola, Ministry of Interior of Czech Republic
Harold Hoffman, University Krems, Austria
Czeslaw Jedrzejek, Poznan University of Technology, Poland
Manuel Maarek, INRIA Grenoble, Rhone-Alpes
Michael Sonntag, Johannes Kepler University Linz, Austria
Vit Stastny, Ministry of Interior of Czech Republic

I thank the participants for their comments and look forward to continuing the discussions which we started in the tutorial.
At the link, one can find the slides. Comments are very welcome. The file is 2.2MB. The slides were originally prepared using Open Office’s Impress, then converted to PowerPoint.
Natural Language Processing Techniques for Managing Legal Resources on the Semantic Web
There is a bit more in the slides than was presented at the tutorial, covering in addition ontologies, parsers, and semantic interpreters.
In the coming weeks, I will make available additional detailed instructions as well as gazetteers and JAPE rules. I also plan to continue to add additional text mining materials.
By Adam Wyner
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Attribution-Non-Commercial-Share Alike 2.0

Tutorial on NLP techniques for managing legal resources on the Semantic Web

Next week, 16 December 2009, I am giving a three hour tutorial at JURIX (International Conference on Legal Knowledge and Information Systems) in Rotterdam, The Netherlands on Natural Language Processing Techniques for Managing Legal Resources on the Semantic Web. The tutorial description appears below. Further material from the tutorial will be presented on the blog.
Legal resources such as legislation, public notices, and case law are increasingly available on the internet. To be automatically processed by web services, the resources must be annotated using semantic web technologies such as XML, RDF, and ontologies. However, manual annotation is labour and knowledge intensive. Using natural language processing techniques and systems (NLP), a significant portion of these resources can be automatically annotated. In this tutorial, we outline the motivations and objectives of NLP, give an overview of several accessible systems (General Architecture on Text Engineering, C&C/Boxer, Attempto Controlled English), provide examples of processing legal resources, and discuss future directions in this area.