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On the semantics of noun compounds
TLDR
This paper provides new insights on the semantic characteristics of two and three noun compounds and presents several models for the bracketing and the semantic classification of noun compounds. Expand
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Models for the Semantic Classification of Noun Phrases
TLDR
This paper presents an approach for detecting semantic relations in noun phrases. Expand
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Experiments with Reasoning for Temporal Relations between Events
TLDR
We present three settings where temporal reasoning aids machine learned classifiers of temporal relations: (1) expansion of the dataset used for learning; (2) detection of inconsistencies among the automatically identified relations; and (3) selection among multiple temporal relations. Expand
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COGEX at the Second Recognizing Textual Entailment Challenge
TLDR
This paper proposes a knowledge representation model and a logic proving setting with axioms on demand which proved to be very successful for the recognizing textual entailment task. Expand
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A Semantic Approach to Recognizing Textual Entailment
TLDR
Exhaustive extraction of semantic information from text is one of the formidable goals of state-of-the-art NLP systems. Expand
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RSDC ’ 08 : Tag Recommendations using Bookmark Content
A variety of factors contribute to a tag being assigned by a us er to a document that he or she bookmarked. Textual information pre sent in a URL’s title, a user’s description of a document, or aExpand
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Automatic Answer Validation using COGEX
TLDR
This paper reports the performance of Language Computer Corporation's natural language logic prover for the English and Spanish subtasks of the Answer Validation Exercise. Expand
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COGEX at RTE 3
TLDR
This paper reports on LCC's participation at the Third PASCAL Recognizing Textual Entailment Challenge. Expand
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A Temporally-Enhanced PowerAnswer in TREC 2006
TLDR
This paper reports on Language Computer Corporation’s participation in the Question Answering track at TREC 2006. Expand
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Semi-Automatic Domain Ontology Creation from Text Resources
TLDR
We present a generalized and improved procedure to automatically extract deep semantic information from text resources and rapidly create semantically-rich domain ontologies while keeping the manual intervention to a minimum. Expand
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