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Domain-specific coreference resolution with lexicalized features
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Abstract:
pre-print ; Most coreference resolvers rely heavily on string matching, syntactic properties, and semantic attributes of words, but they lack the ability to make decisions based on individual words. In this paper, we explore the benefits of lexicalized features in the setting of domain-specific coreference resolution. We show that adding lexicalized features to off-the-shelf coreference resolvers yields significant performance gains on four domain-specific data sets and with two types of coreference resolution architectures.
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URL: https://collections.lib.utah.edu/ark:/87278/s674011x
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Conundrums in noun phrase coreference resolution: making sense of the state-of-the-art
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Toward completeness in concept extraction and classification
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Corpus-based semantic lexicon induction with web-based corroboration
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Unified model of phrasal and sentential evidence for information extraction
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Semantic class learning from the web with hyponym pattern linkage graphs
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Effective information extraction with semantic affinity patterns and relevant regions
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Exploiting role-identifying nouns and expressions for information extraction
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Learning domain-specific information extraction patterns from the web
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OpinionFinder: a system for subjectivity analysis
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Wilson, Theresa; Hoffmann, Paul; Somasundaran, Swapna; Kessler, Jason; Wiebe, Janyce; Choi, Yejin; Cardie, Claire; Patwardhan, Siddharth; Riloff, Ellen M.. - : Association for Computational Linguistics, 2005
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Identifying sources of opinions with conditional random fields and extraction patterns
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Unsupervised learning of contextual role knowledge for coreference resolution
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Learning subjective nouns using extraction pattern bootstrapping
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Exploiting strong syntactic heuristics and co-training to learn semantic lexicons
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Bootstrapping method for learning semantic lexicons using extraction pattern contexts
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Inducing information extraction systems for new languages via cross-language projection
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Looking under the hood: tools for diagnosing your question answering engine
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Rule-based question answering system for reading comprehension tests
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