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Filters: Format: "application/pdf" Collection: "ir_uspace" Subject: "Information extraction"
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CreatorTitleDescriptionSubjectDate
1 Riloff, Ellen M.Automatically constructing a dictionary for information extraction tasksKnowledge-based natural language processing systems have achieved good success with certain tasks but they are often criticized because they depend on a domain-specific dictionary that requires a great deal of manual knowledge engineering. This knowledge engineering bottleneck makes knowledge-based ...Information extraction; Dictionary construction; Knowledge-based systems; AutoSlog; Domain-specific dictionary1993
2 Riloff, Ellen M.Automatically generating extraction patterns from untagged textMany corpus-based natural language processing systems rely on text corpora that have been manually annotated with syntactic or semantic tags. In particular, all previous dictionary construction systems for information extraction have used an annotated training corpus or some form of annotated input...Information extraction; Automatically generating; Extraction patterns; Untagged text; Corpus-based; AutoSlog-TS; AutoSlog system; MUC-4; Dictionary construction1996
3 Riloff, Ellen M.Effective information extraction with semantic affinity patterns and relevant regionsWe present an information extraction system that decouples the tasks of finding relevant regions of text and applying extraction patterns. We create a self-trained relevant sentence classifier to identify relevant regions, and use a semantic affinity measure to automatically learn domain-relevant ex...Information extraction; Semantic affinity patterns; Relevant regions; MUC-4 terrorism corpus; ProMed disease outbreak stories2007
4 Riloff, Ellen M.Empirical study of automated dictionary construction for information extraction in three domainsA primary goal of natural language processing researchers is to develop a knowledge-based natural language processing (NLP) system that is portable across domains. However, most knowledge-based NLP systems rely on a domain-specific dictionary of concepts, which represents a substantial knowledge-en...Information extraction; AutoSlog; Across domains1996
5 Riloff, Ellen M.Exploiting role-identifying nouns and expressions for information extractionWe present a new approach for extraction pattern learning that exploits role-identifying nouns, which are nouns whose semantics reveal the role that they play in an event (e.g., an "assassin" is a perpetrator). Given a few seed nouns, a bootstrapping algorithm automatically learns role-identifying ...Information extraction; Role-identifying; Nouns; Expressions; Pattern learning; Basilisk bootstrapping algorithm2007
6 Riloff, Ellen M.Exploiting subjectivity classification to improve information extractionInformation extraction (IE) systems are prone to false hits for a variety of reasons and we observed that many of these false hits occur in sentences that contain subjective language (e.g., opinions, emotions, and sentiments). Motivated by these observations, we explore the idea of using subjecti...Subjectivity classification; Information extraction; Subjectivity analysis; MUC-42005
7 Riloff, Ellen M.Inducing information extraction systems for new languages via cross-language projectionInformation extraction (IE) systems are costly to build because they require development texts, parsing tools, and specialized dictionaries for each application domain and each natural language that needs to be processed. We present a novel method for rapidly creating IE systems for new languages by...Information extraction; IE systems; Cross-language projection; English; French2002
8 Riloff, Ellen M.Information extraction as a stepping stone toward story understandingHistorically story understanding systems have depended on a great deal of handcrafted knowledge. Natural language understanding systems that use conceptual knowledge structures (Schank and Abelson 1977; Cullingford 1978; Wilensky 1978; Carbonell 1979; Lehnert 1981; Kolodner 1983) typically rely on ...Information extraction; Story understanding1999
9 Riloff, Ellen M.Learning dictionaries for information extraction by multi-level bootstrappingInformation extraction systems usually require two dictionaries: a semantic lexicon and a dictionary of extraction patterns for the domain. We present a multilevel bootstrapping algorithm that generates both the semantic lexicon and extraction patterns simultaneously. As input, our technique requir...Information extraction; Extraction patterns; Multi-level bootstrapping; Learning dictionaries1999
10 Riloff, Ellen M.Learning domain-specific information extraction patterns from the webMany information extraction (IE) systems rely on manually annotated training data to learn patterns or rules for extracting information about events. Manually annotating data is expensive, however, and a new data set must be annotated for each domain. So most IE training sets are relatively small. C...Information extraction; Domain-specific; Annotated training sets; MUC-42006
11 Riloff, Ellen M.Unified model of phrasal and sentential evidence for information extractionInformation Extraction (IE) systems that extract role fillers for events typically look at the local context surrounding a phrase when deciding whether to extract it. Often, however, role fillers occur in clauses that are not directly linked to an event word. We present a new model for event extract...Information extraction; Phrasal evidence; Sentential evidence; Role fillers; Event extraction; Sentential event recognizer; Plausible roll-filler recognizer2009
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