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Filters: Format: "application/pdf" Collection: "ir_uspace" Subject: "MUC-4"
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CreatorTitleDescriptionSubjectDate
1 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
2 Riloff, Ellen M.Corpus-based identification of non-anaphoric noun phrasesCoreference resolution involves finding antecedents for anaphoric discourse entities, such as definite noun phrases. But many definite noun phrases are not anaphoric because their meaning can be understood from general world knowledge (e.g., "the White House" or "the news media"). We have develope...Corpus-based identification; Non-anaphoric noun phrases; Coreference resolution; MUC-4; Discourse entity; DE1999
3 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
4 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
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