R
Raymond J. Mooney
Researcher at University of Texas at Austin
Publications - 320
Citations - 35237
Raymond J. Mooney is an academic researcher from University of Texas at Austin. The author has contributed to research in topics: Natural language & Parsing. The author has an hindex of 86, co-authored 308 publications receiving 32776 citations. Previous affiliations of Raymond J. Mooney include University of Illinois at Urbana–Champaign.
Papers
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Proceedings Article
Learning to interpret natural language navigation instructions from observations
David L. Chen,Raymond J. Mooney +1 more
TL;DR: A system that learns to transform natural-language navigation instructions into executable formal plans by using a learned lexicon to refine inferred plans and a supervised learner to induce a semantic parser.
Journal ArticleDOI
Adaptive name matching in information integration
TL;DR: The authors compare and describe methods for combining and learning textual similarity measures for name matching that are essential for information integration.
Proceedings Article
Subsequence Kernels for Relation Extraction
Raymond J. Mooney,Razvan Bunescu +1 more
TL;DR: A new kernel method for extracting semantic relations between entities in natural language text, based on a generalization of subsequence kernels, is presented, which uses three types of subsequent patterns that are typically employed innatural language to assert relationships between two entities.
Journal ArticleDOI
Comparative experiments on learning information extractors for proteins and their interactions
Razvan Bunescu,Ruifang Ge,Rohit J. Kate,Edward M. Marcotte,Raymond J. Mooney,Arun K. Ramani,Yuk Wah Wong +6 more
TL;DR: The results show that it is promising to use machine learning to automatically build systems for extracting information from biomedical text with higher precision than manually-developed rules.
Proceedings Article
Multi-Prototype Vector-Space Models of Word Meaning
TL;DR: Experimental comparisons to human judgements of semantic similarity for both isolated words as well as words in sentential contexts demonstrate the superiority of this approach over both prototype and exemplar based vector-space models.