A system for text analysis and lexical knowledge acquisition
Data & Knowledge Engineering
Computational Intelligence
Word association norms, mutual information, and lexicography
Computational Linguistics
Theory of Syntactic Recognition for Natural Languages
Theory of Syntactic Recognition for Natural Languages
Using Word Association for Syntactic Disambiguation
AI*IA Proceedings of the 2nd Congress of the Italian Association for Artificial Intelligence on Trends in Artificial Intelligence
Structural ambiguity and lexical relations
Computational Linguistics - Special issue on using large corpora: I
Retrieving collocations from text: Xtract
Computational Linguistics - Special issue on using large corpora: I
Computational lexicons: the neat examples and the odd exemplars
ANLC '92 Proceedings of the third conference on Applied natural language processing
Automatic learning for semantic collocation
ANLC '92 Proceedings of the third conference on Applied natural language processing
A generative grammar approach for the morphologic and morphosyntactic analysis of Italian
EACL '87 Proceedings of the third conference on European chapter of the Association for Computational Linguistics
Structural ambiguity and lexical relations
ACL '91 Proceedings of the 29th annual meeting on Association for Computational Linguistics
From N-grams to collocations: an evaluation of Xtract
ACL '91 Proceedings of the 29th annual meeting on Association for Computational Linguistics
Distributional clustering of English words
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
Automatically extracting and representing collocations for language generation
ACL '90 Proceedings of the 28th annual meeting on Association for Computational Linguistics
Noun classification from predicate-argument structures
ACL '90 Proceedings of the 28th annual meeting on Association for Computational Linguistics
Estimating upper and lower bounds on the performance of word-sense disambiguation programs
ACL '92 Proceedings of the 30th annual meeting on Association for Computational Linguistics
Acquisition of lexical information: from a large textual Italian corpus
COLING '90 Proceedings of the 13th conference on Computational linguistics - Volume 3
Word-sense disambiguation using statistical models of Roget's categories trained on large corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Tagging for learning: collecting thematic relations from corpus
COLING '90 Proceedings of the 13th conference on Computational linguistics - Volume 1
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Unsupervised named entity recognition using syntactic and semantic contextual evidence
Computational Linguistics
Semantic tagging of unknown proper nouns
Natural Language Engineering
Automatic semantic tagging of unknown proper names
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Symbolic word clustering for medium-size corpora
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
Multilingual collocation extraction: issues and solutions
MLRI '06 Proceedings of the Workshop on Multilingual Language Resources and Interoperability
Two-Word Collocation Extraction Using Monolingual Word Alignment Method
ACM Transactions on Intelligent Systems and Technology (TIST)
| Hi-index | 0.00 |
Collocational analysis is the basis of many studies on lexical acquisition. Collocations are extracted from corpora using more or less shallow processing techniques, that span from purely statistical methods to patial parsers. Our point is that, despite one of the objectives of collocational analysis is to acquire high-coverage lexical data at low human cost, this is often not the case. Human work is in fact required for the initial training of most statistically based methods. A more serious problem is that shallow processing techniques produce a noise that is not acceptable for a fully automated system.We propose in this paper a not-so-shallow parsing strategy that reliably detects binary and ternary relations among words. We show that adding more syntactic knowledge to the recipe significantly improves the recall and precision of the detected collocations, regardless of any subsequent statistical computation, while still meeting the computational requirements of corpus parsers.