Learning to resolve natural language ambiguities: a unified approach
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Generating indicative-informative summaries with sumUM
Computational Linguistics - Summarization
Automatic labeling of semantic roles
Computational Linguistics
An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Discovering word senses from text
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Word-sense disambiguation using statistical methods
ACL '91 Proceedings of the 29th annual meeting on Association for Computational Linguistics
A rule-based approach to prepositional phrase attachment disambiguation
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 2
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Extracting paraphrases from a parallel corpus
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Improving machine learning approaches to coreference resolution
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
An unsupervised approach to prepositional phrase attachment using contextually similar words
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
A Computational Framework to Integrate Different Semantic Resources
TSD '08 Proceedings of the 11th international conference on Text, Speech and Dialogue
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This paper introduces PhraseNet, a context-sensitive lexical semantic knowledge base system. Based on the supposition that semantic proximity is not simply a relation between two words in isolation, but rather a relation between them in their context, English nouns and verbs, along with contexts they appear in, are organized in PhraseNet into Consets; Consets capture the nderying lexical concept, and are connected with several semantic relations that respect contextually sensitive lexical information. PhraseNet makes use of WordNet as an important knowledge source. It enhances a WordNet synset with its contextual information and refines its relational structure by maintaining only those relations that respect contextual constraints. The contextual information allows for supporting more functionalities compared with those of WordNet. Natural language researchers as well as linguists and language learners can gain from accessing PhraseNet with a word token and its context, to retrieve relevant semantic information.We describe the design and construction of PhraseNet and give preliminary experimental evidence to its usefulness for NLP researches.