Deadline Extended to 17 January: Call for Papers for ICAIL 2011

[NOTE: The call for papers submission deadline has been extended to 17 January 2011, according to @JackGConrad.]

A call for papers has been issued for ICAIL 2011: The 13th International Conference on Artificial Intelligence and Law, to be held 6-10 June 2011 at the University of Pittsburgh School of Law in Pittsburgh, Pennsylvania, USA.

The conference is organized by IAAIL: The International Association for Artificial Intelligence and Law.

A mentoring program is being offered for authors wishing to submit papers to the conference.

Here are the submission deadlines:

• “Mentoring program request deadline: November 8, 2010
• Mentoring program paper deadline: November 15, 2010
• Submission of workshop and tutorial proposals: December 6, 2010
• Submission of abstracts (optional): January 3, 2011″
• Submission of papers extended deadline: January 17, 2011

Papers are invited on the following topics:

• “Formal and computational models of legal reasoning
• Knowledge acquisition techniques for the legal domain, including natural language processing and data mining
• Computational models of argumentation and decision making
• Legal knowledge representation including legal ontologies and common sense knowledge
• Computational models of evidential reasoning
• Modeling norms for multi-agent systems
• Modeling negotiation and contract formation
• Computational models of case-based legal reasoning
• Conceptual or model-based legal information retrieval
• Automated information extraction from legal databases and texts
• Intelligent legal tutoring systems
• Intelligent support systems for the legal domain
• E-discovery and e-disclosure
• Automatic legal text classification and summarization
• Machine learning and data mining applied to legal databases”

For more information, please see the call for papers.

HT Jack G. Conrad.

December 6 Deadline: ICAIL 2011 Workshop & Tutorial Proposals

[NOTE: 6 December 2010 is the deadline for submitting workshop and tutorial proposals.]

A call for papers has been issued for ICAIL 2011: The 13th International Conference on Artificial Intelligence and Law, to be held 6-10 June 2011 at the University of Pittsburgh School of Law in Pittsburgh, Pennsylvania, USA.

The conference is organized by IAAIL: The International Association for Artificial Intelligence and Law.

A mentoring program is being offered for authors wishing to submit papers to the conference.

Here are the remaining submission deadlines:

• “Submission of workshop and tutorial proposals: December 6, 2010
• Submission of abstracts (optional): January 3, 2011
• Submission of papers deadline: January 10, 2011″

Papers are invited on the following topics:

• “Formal and computational models of legal reasoning
• Knowledge acquisition techniques for the legal domain, including natural language processing and data mining
• Computational models of argumentation and decision making
• Legal knowledge representation including legal ontologies and common sense knowledge
• Computational models of evidential reasoning
• Modeling norms for multi-agent systems
• Modeling negotiation and contract formation
• Computational models of case-based legal reasoning
• Conceptual or model-based legal information retrieval
• Automated information extraction from legal databases and texts
• Intelligent legal tutoring systems
• Intelligent support systems for the legal domain
• E-discovery and e-disclosure
• Automatic legal text classification and summarization
• Machine learning and data mining applied to legal databases”

For more information, please see the call for papers.

HT Jack G. Conrad.

Call for Papers: ICAIL 2011

A call for papers has been issued for ICAIL 2011: The 13th International Conference on Artificial Intelligence and Law, to be held 6-10 June 2011 at the University of Pittsburgh School of Law in Pittsburgh, Pennsylvania, USA.

The conference is organized by IAAIL: The International Association for Artificial Intelligence and Law.

A mentoring program is being offered for authors wishing to submit papers to the conference.

Here are the submission deadlines:

• “Mentoring program request deadline: November 8, 2010
• Mentoring program paper deadline: November 15, 2010
• Submission of workshop and tutorial proposals: December 6, 2010
• Submission of abstracts (optional): January 3, 2011
• Submission of papers deadline: January 10, 2011″

Papers are invited on the following topics:

• “Formal and computational models of legal reasoning
• Knowledge acquisition techniques for the legal domain, including natural language processing and data mining
• Computational models of argumentation and decision making
• Legal knowledge representation including legal ontologies and common sense knowledge
• Computational models of evidential reasoning
• Modeling norms for multi-agent systems
• Modeling negotiation and contract formation
• Computational models of case-based legal reasoning
• Conceptual or model-based legal information retrieval
• Automated information extraction from legal databases and texts
• Intelligent legal tutoring systems
• Intelligent support systems for the legal domain
• E-discovery and e-disclosure
• Automatic legal text classification and summarization
• Machine learning and data mining applied to legal databases”

For more information, please see the call for papers.

HT Jack G. Conrad.

Gonçalves & Quaresma on Multilingual Text Classification Through Combination of Monolingual Classifiers

Teresa Gonçalves and Professor Paulo Quaresma, both of Universidade de Évora Departamento de Informática, have published Multilingual Text Classification Through Combination of Monolingual Classifiers, in LOAIT 2010: Proceedings of the 4th Workshop on Legal Ontologies and Artificial Intelligence Techniques, European University Institute, Fiesole, Florence, Italy, July 7th, 2010, at 29-38 (Enrico Francesconi, Simonetta Montemagni, Piercarlo Rossi, and Daniela Tiscornia eds., 2010). Here is the abstract:

With the globalization trend there is a big amount of documents written in different languages. If these polylingual documents are already organized into existing categories one can deliver a learning model to classify newly arrived polylingual documents. Despite being able to adopt a simple approach by considering the problem as multiple independent monolingual text classification problems, this approach fails to use the opportunity offered by polylingual training documents to improve the effectiveness of the classifier. This paper proposes a method to combine different monolingual classifiers in order to get a new classifier as good as the best monolingual one having also the ability to deliver the best performance measures possible (precision, recall and F1). The proposed methodology was applied to a corpus of legal documents – from the EUR-Lex site – and was evaluated. The obtained results were quite good, indicating that combining different mono-lingual classifiers may be a promising approach to reach the best performance for each category independently of the language.

Privault et al. on a New Tangible User Interface for Machine Learning Document Review

Caroline Privault and colleagues, all of Xerox Research Center Europe, have published A New Tangible User Interface for Machine Learning Document Review, forthcoming in Artificial Intelligence and Law. Here is the abstract:

This paper describes a tool for assisting lawyers and paralegal teams during document review in eDiscovery. The tool combines a machine learning technology (CategoriX) and advanced multi-touch interface capable of not only addressing the usual cost, time and accuracy issues in document review, but also of facilitating the work of the review teams by capitalizing on the intelligence of the reviewers and enabling collaborative work.

Bruckschen et al. on Named Entity Recognition in the Legal Domain for Ontology Population

Mírian Bruckschen of Pontifícia Universidade Católica do Rio Grande do Sul, and colleagues, will present a paper entitled Named Entity Recognition in the Legal Domain for Ontology Population (for the full text of the paper, click here for the conference proceedings in PDF and scroll down to the page numbered 16) at SPLeT 2010: The 3rd Workshop on Semantic Processing of Legal Texts, to be held 23 May 2010 in Malta.

The workshop is part of LREC 2010: The 7th International Conference on Language Resources and Evaluation.

Here is the abstract of the paper:

This paper presents the overall problem of privacy risk assessment in the software industry and the difficulty to deal with all normative sources that regulate privacy matters. This problem encompasses the hard task of representing all the relevant information and keep it updated. Ontologies are the main mechanism for domain-specific knowledge representation in the Semantic Web context, but their manual maintenance is expensive and error-prone. Following the ontology learning trend, this paper presents an approach to automatically populate a legal ontology from legal texts through the Named Entity Recognition task and an experiment on this approach. Legal ontologies have been an active topic of research for quite a while, but on specific domains such as data privacy there is still a lack of such resources. The experiment described in this paper is run over a corpus of legal and normative documents for privacy, shows promising results and presents opportunities for the continuation of this research.

Quaresma & Gonçalves on Using Linguistic Information and Machine Learning Techniques to Identify Entities from Juridical Documents

Professor Paulo Quaresma and Teresa Gonçalves, both of Universidade de Évora Departamento de Informática, have published Using Linguistic Information and Machine Learning Techniques to Identify Entities from Juridical Documents, in Semantic Processing of Legal Texts: Where the Language of Law Meets the Law of Language 27-43 (Enrico Francesconi et al. eds., 2010). (Click here for a description of the print version of the book.)

Here is the abstract of the paper:

Information extraction from legal documents is an important and open problem. A mixed approach, using linguistic information and machine learning techniques, is described in this paper. In this approach, top-level legal concepts are identified and used for document classification using Support Vector Machines. Named entities, such as, locations, organizations, dates, and document references, are identified using semantic information from the output of a natural language parser. This information, legal concepts and named entities, may be used to populate a simple ontology, allowing the enrichment of documents and the creation of high-level legal information retrieval systems.

The proposed methodology was applied to a corpus of legal documents – from the EUR-Lex site – and it was evaluated. The obtained results were quite good and indicate this may be a promising approach to the legal information extraction problem.

Dozier et al. on Named Entity Recognition and Resolution in Legal Text

Christopher Dozier and colleagues, all of Thomson Reuters Research and Development, have published Named Entity Recognition and Resolution in Legal Text, in Semantic Processing of Legal Texts: Where the Language of Law Meets the Law of Language 27-43 (Enrico Francesconi et al. eds., 2010). (Click here for a description of the print version of the book.)

Here is the abstract of the paper:

Named entities in text are persons, places, companies, etc. that are explicitly mentioned in text using proper nouns. The process of finding named entities in a text and classifying them to a semantic type, is called named entity recognition. Resolution of named entities is the process of linking a mention of a name in text to a pre-existing database entry. This grounds the mention in something analogous to a real world entity. For example, a mention of a judge named Mary Smith might be resolved to a database entry for a specific judge of a specific district of a specific state. This recognition and resolution of named entities can be leveraged in a number of ways including providing hypertext links to information stored about a particular judge: their education, who appointed them, their other case opinions, etc.

This paper discusses named entity recognition and resolution in legal documents such as US case law, depositions, and pleadings and other trial documents. The types of entities include judges, attorneys, companies, jurisdictions, and courts. We outline three methods for named entity recognition, lookup, context rules, and statistical models. We then describe an actual system for finding named entities in legal text and evaluate its accuracy. Similarly, for resolution, we discuss our blocking techniques, our resolution features, and the supervised and semi-supervised machine learning techniques we employ for the final matching.