Memory and context for language interpretation
Memory and context for language interpretation
Computational Linguistics
A probabilistic account of logical metonymy
Computational Linguistics
The semantic interpretation of compound nominals
The semantic interpretation of compound nominals
Semi-automatic recognition of noun modifier relationships
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Corpus statistics meet the noun compound: some empirical results
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Accurate unlexicalized parsing
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Large-scale extraction and use of knowledge from text
Proceedings of the fifth international conference on Knowledge capture
The Stanford typed dependencies representation
CrossParser '08 Coling 2008: Proceedings of the workshop on Cross-Framework and Cross-Domain Parser Evaluation
Open knowledge extraction through compositional language processing
STEP '08 Proceedings of the 2008 Conference on Semantics in Text Processing
The knowledge required to interpret noun compounds
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
On the semantics of noun compounds
Computer Speech and Language
A taxonomy, dataset, and classifier for automatic noun compound interpretation
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
SemEval-2010 task 8: Multi-way classification of semantic relations between pairs of nominals
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
SemEval-2010 task 9: The interpretation of noun compounds using paraphrasing verbs and prepositions
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
Semantic enrichment of text with background knowledge
FAM-LbR '10 Proceedings of the NAACL HLT 2010 First International Workshop on Formalisms and Methodology for Learning by Reading
Filling knowledge gaps in text for machine reading
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
The PASCAL recognising textual entailment challenge
MLCW'05 Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment
Using verbs to characterize noun-noun relations
AIMSA'06 Proceedings of the 12th international conference on Artificial Intelligence: methodology, Systems, and Applications
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A predicate is usually omitted from text when it is highly predictable from the context. This omission is due to the effort optimization that humans perform during the language generation process. Authors omit the information that they know the addressee is able to recover effortlessly. Most noun-noun structures including genitives and compounds are result of this process. The goal of this work is to generate automatically and without supervision the paraphrases that make explicit the omitted predicate in these noun-noun structures. The method is general enough to address also the cases were components are Named Entities. The resulting paraphrasing axioms are necessary for recovering the semantics of a text, and therefore, useful for applications such as Question Answering.