Machine Learning
Class-Based Construction of a Verb Lexicon
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Head-driven statistical models for natural language parsing
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Structural ambiguity and lexical relations
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Automatic extraction of subcategorization from corpora
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Automatic retrieval and clustering of similar words
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
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Automatic extraction of subcategorization frames for Czech
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
Combining distributional and morphological information for part of speech induction
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
An expert lexicon approach to identifying English phrasal verbs
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Learning to distinguish PP arguments from adjuncts
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
The Proposition Bank: An Annotated Corpus of Semantic Roles
Computational Linguistics
The Notion of Argument in Prepositional Phrase Attachment
Computational Linguistics
Exploiting a verb lexicon in automatic semantic role labelling
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
A global joint model for semantic role labeling
Computational Linguistics
The importance of syntactic parsing and inference in semantic role labeling
Computational Linguistics
Towards robust semantic role labeling
Computational Linguistics
Prepositions in applications: A survey and introduction to the special issue
Computational Linguistics
Unsupervised recognition of literal and non-literal use of idiomatic expressions
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Unsupervised discovery of a statistical verb lexicon
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Feature generation for text categorization using world knowledge
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Unsupervised argument identification for Semantic Role Labeling
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
Multi-word expression identification using sentence surface features
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
Introduction to the CoNLL-2005 shared task: semantic role labeling
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Improved unsupervised POS induction through prototype discovery
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Type level clustering evaluation: new measures and a POS induction case study
CoNLL '10 Proceedings of the Fourteenth Conference on Computational Natural Language Learning
Unsupervised semantic role induction with graph partitioning
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Learning syntactic verb frames using graphical models
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Methodological Review: Approaches to verb subcategorization for biomedicine
Journal of Biomedical Informatics
Statistical metaphor processing
Computational Linguistics
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The core-adjunct argument distinction is a basic one in the theory of argument structure. The task of distinguishing between the two has strong relations to various basic NLP tasks such as syntactic parsing, semantic role labeling and subcategorization acquisition. This paper presents a novel unsupervised algorithm for the task that uses no supervised models, utilizing instead state-of-the-art syntactic induction algorithms. This is the first work to tackle this task in a fully unsupervised scenario.