Calls for Papers: Workshops @ ICAIL 2011

Calls for papers, with diverse submission deadlines, have been issued for the workshops at ICAIL 2011: The International Conference on Artificial Intelligence and Law; the workshops are scheduled to be held 6 and 10 June 2011, in Pittsburgh, Pennsylvania, USA.

DESI IV: Workshop on Setting Standards for Searching Electronically Stored Information in Discovery Proceedings, 6 June 2011. Deadlines:

• 1 April 2011: Research papers;
• 22 April 2011: Position papers.

Workshop on Agent Model-Based Reasoning in Law, 6 June 2011. Deadline:

• 14 March 2011.

Computational Law: A Bridge Towards the Business Rules, 6 June 2011. Deadline:

• 20 April 2011.

AI & Evidential Inference, 10 June 2011. Deadline:

• TBA

AHLTL 2011: Applying Human Language Technology to the Law, 10 June 2011. Deadline:

• 31 March 2011.

Coherence 2011: Artificial Intelligence, Coherence, and Judicial Reasoning, 10 June 2011. Deadlines:

• 15 April 2011: Abstracts;
• 3 June 2011: Full papers.

HT JURIX.

Call for Papers: Workshop on Applying Human Language Technology to the Law

A call for papers — with submission deadline of 31 March 2011 — has been issued for AHLTL 2011: Applying Human Language Technology to the Law, a workshop to be held 10 June 2011, at ICAIL 2011: The Thirteenth International Conference on Artificial Intelligence and Law, in Pittsburgh, Pennsylvania, USA.

[If the call for papers or the workshop Website is down, click here for the cached version.]

Papers are invited on the following topics:

The workshop will focus on extraction of information from legal text, representations of legal language (ontologies and semantic translations), and dialogic aspects. While information extraction and retrieval are crucial areas, the workshop emphasises syntactic, semantic, and dialogic aspects of legal information processing.

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.
Multilingual aspects of legal text semantic processing.
Legal thesauri mapping.
Automatic Classification of legal documents.
Automated parsing and translation of natural language arguments into a logical formalism.
Linguistically-oriented XML mark up of legal arguments.
Computational theories of argumentation that are suitable to natural language.
Controlled language systems for law.
Name matching and alias detection.
Dialogue protocols and systems for legal discussion.

For more information, please see the call for papers.

HT Dr. Adam Wyner.

Surdeanu, Nallapati, & Manning on Legal Claim Identification: Information Extraction with Hierarchically Labeled Data

Dr. Mihai Surdeanu, Dr. Ramesh Nallapati, and Professor Christopher Manning, all of the Stanford University Department of Computer Science Natural Language Processing Group, will present a paper entitled Legal Claim Identification: Information Extraction with Hierarchically Labeled Data (for the full text of the paper, click here for the conference proceedings in PDF and scroll down to the page numbered 22) 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 introduces a novel Information Extraction problem, where only parts of documents have relevance and linguistic annotations are available only for these segments. The data is hierarchical: the top layer marks the relevant text segments and the bottom layer annotates domain-specific entity mentions, but only in the segments marked as relevant in the top layer. We investigate this problem in the legal domain, where we extract the text corresponding to litigation claims and entity mentions such as patents and laws in each claim. Because entity mentions are not labeled outside claims in training data, a top-down approach that extracts claims first and entity mentions next seems the most natural. However, we show that other models are superior. Using a simple semi-supervised approach we implement a bottom-up Conditional Random Field model; we also implement a joint hierarchical CRF using a combination of pseudo-likelihood and Gibbs sampling. We show that both these models significantly outperform the top-down approach.