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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 9 of 9 1 An extended model of natural logic Bill Maccartney; Christopher D. Manning In: http://aclweb.org/anthology-new/W/W09/W09-3714.pdf (2009) BASE Show details 2 A phrase-based alignment model for natural language inference Bill Maccartney; Michel Galley; Christopher D. Manning In: http://nlp.stanford.edu/~mgalley/papers/emnlp08-nli.pdf (2008) BASE Show details 3 Modeling semantic containment and exclusion in natural language inference Bill Maccartney; Christopher D. Manning In: http://aclweb.org/anthology-new/C/C08/C08-1066.pdf (2008) BASE Show details 4 A phrase-based alignment model for natural language inference Bill Maccartney; Michel Galley; Christopher D. Manning In: http://www.aclweb.org/anthology-new/D/D08/D08-1084.pdf (2008) BASE Show details 5 Robust textual inference using diverse knowledge sources Rajat Raina; Aria Haghighi; Christopher Cox... In: http://www.stanford.edu/~jrfinkel/papers/pascalRTE.pdf (2005) BASE Show details 6 Robust textual inference using diverse knowledge sources Rajat Raina; Aria Haghighi; Christopher Cox... In: http://nlp.stanford.edu/cmanning/papers/rte.pdf (2005) BASE Show details 7 Robust Graph Alignment Methods for Textual Inference and Machine Reading Marie-catherine De Marneffe; Trond Grenager; Bill Maccartney; Daniel Cer; Daniel Ramage; Chloé Kiddon; Christopher D. Manning In: http://www.aaai.org/Papers/Symposia/Spring/2007/SS-07-06/SS07-06-007.pdf Abstract: This paper presents our work on textual inference and situates it within the context of the larger goals of machine reading. The textual inference task is to determine if the meaning of one text can be inferred from the meaning of another combined with background knowledge. Most existing work either provides only very limited text understanding by using bag-of-words lexical similarity models or suffers from the brittleness typical of complex natural language understanding systems. Our system generates semantic graphs as a representation of the meaning of a text. This paper presents new results for aligning pairs of semantic graphs, and proposes the application of natural logic to derive inference decisions from those aligned pairs. We consider this work as first steps toward a system able to demonstrate broad-coverage text understanding and learning abilities. URL: http://www.aaai.org/Papers/Symposia/Spring/2007/SS-07-06/SS07-06-007.pdf http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.329.1509 BASE Hide details 8 Modeling Semantic Containment and Exclusion in Natural Language Inference Bill Maccartney In: http://nlp.stanford.edu/~wcmac/papers/natlog-coling08.pdf BASE Show details 9 A Phrase-Based Alignment Model for Natural Language Inference Bill Maccartney; Michel Galley; Christopher D. Manning In: http://nlp.stanford.edu/~wcmac/papers/nli-alignment-emnlp08.pdf BASE Show details

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