Machine Learning
A maximum entropy approach to natural language processing
Computational Linguistics
Machine Learning
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Learning and Inference for Clause Identification
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Maximum Entropy Markov Models for Information Extraction and Segmentation
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Bunsetsu identification using category-exclusive rules
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Automatic summarisation of legal documents
ICAIL '03 Proceedings of the 9th international conference on Artificial intelligence and law
A model-theoretic coreference scoring scheme
MUC6 '95 Proceedings of the 6th conference on Message understanding
Filtering-Ranking Perceptron Learning for Partial Parsing
Machine Learning
Chunking with maximum entropy models
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
An empirical evaluation of knowledge sources and learning algorithms for word sense disambiguation
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Information extraction from research papers using conditional random fields
Information Processing and Management: an International Journal
Semantic role labeling via integer linear programming inference
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Semantic parsing with structured SVM ensemble classification models
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Deep semantic interpretations of legal texts
Proceedings of the 11th international conference on Artificial intelligence and law
Automatic detection of arguments in legal texts
Proceedings of the 11th international conference on Artificial intelligence and law
New Frontiers in Artificial Intelligence
Towards Semantic Interpretation of Legal Modifications through Deep Syntactic Analysis
Proceedings of the 2008 conference on Legal Knowledge and Information Systems: JURIX 2008: The Twenty-First Annual Conference
NLP-based metadata extraction for legal text consolidation
Proceedings of the 12th International Conference on Artificial Intelligence and Law
Labeled pseudo-projective dependency parsing with support vector machines
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
Parsing syntactic and semantic dependencies with two single-stage maximum entropy models
CoNLL '08 Proceedings of the Twelfth Conference on Computational Natural Language Learning
Using a maximum entropy model to build segmentation lattices for MT
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Global inference for sentence compression an integer linear programming approach
Journal of Artificial Intelligence Research
Automatic extraction of definitions from German court decisions
IEBeyondDoc '06 Proceedings of the Workshop on Information Extraction Beyond The Document
Concise integer linear programming formulations for dependency parsing
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
Proceedings of the 2010 conference on Legal Knowledge and Information Systems: JURIX 2010: The Twenty-Third Annual Conference
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Legal language and legal knowledge management applications
Semantic Processing of Legal Texts
Approaches to text mining arguments from legal cases
Semantic Processing of Legal Texts
A methodology to create legal ontologies in a logic programming information retrieval system
Law and the Semantic Web
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Analyzing logical structures of texts is important to understanding natural language, especially in the legal domain, where legal texts have their own specific characteristics. Recognizing logical structures in legal texts does not only help people in understanding legal documents, but also in supporting other tasks in legal text processing. In this article, we present a new task, learning logical structures of paragraphs in legal articles, which is studied in research on Legal Engineering. The goals of this task are recognizing logical parts of law sentences in a paragraph, and then grouping related logical parts into some logical structures of formulas, which describe logical relations between logical parts. We present a two-phase framework to learn logical structures of paragraphs in legal articles. In the first phase, we model the problem of recognizing logical parts in law sentences as a multi-layer sequence learning problem, and present a CRF-based model to recognize them. In the second phase, we propose a graph-based method to group logical parts into logical structures. We consider the problem of finding a subset of complete subgraphs in a weighted-edge complete graph, where each node corresponds to a logical part, and a complete subgraph corresponds to a logical structure. We also present an integer linear programming formulation for this optimization problem. Our models achieve 74.37% in recognizing logical parts, 80.08% in recognizing logical structures, and 58.36% in the whole task on the Japanese National Pension Law corpus. Our work provides promising results for further research on this interesting task.