Dictionary-based Entity Mention Recognition Algorithm: Difference between revisions
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A [[Dictionary-based Entity Mention Recognition Algorithm]] is an [[Entity Mention Recognition Algorithm]] that makes use of a [[semantic lexicon]]. | A [[Dictionary-based Entity Mention Recognition Algorithm]] is an [[Entity Mention Recognition Algorithm]] that makes use of a [[semantic lexicon]]. | ||
* <B>AKA:</B> [[Instance-based Semantic Tagging]]. | * <B>AKA:</B> [[Dictionary-based Entity Mention Recognition Algorithm|Instance-based Semantic Tagging]]. | ||
* <B>See:</B> [[]]. | * <B>See:</B> [[Heuristic Recognition Algorithm]] [[Semantic Lexicon]]. | ||
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===2012=== | == References == | ||
* ([[2012_EnsemblebasedSemanticLexiconInd|Qadir & Riloff, 2012]]) | |||
** QUOTE: [[2012_EnsemblebasedSemanticLexiconInd|We]] also evaluated the effectiveness of the [[lexicon induction task|induced]] [[lexicon]]s with respect to [[instance-based semantic tagging]]. Our goal was to determine how useful the dictionaries are in two respects: (1) do the lexicons contain words that appear frequently in the domain, and (2) is dictionary look-up sufficient for instance-based labeling? Our bootstrapping processes enforce a constraint that a word can only belong to one semantic class, so if polysemy is common, then dictionary look-up will be problematic.<ref>15Only coarse polysemy across semantic classes is an issue (e.g., “plant” as a living thing vs. a factory).</ref> | === 2012 === | ||
* ([[2012_EnsemblebasedSemanticLexiconInd|Qadir & Riloff, 2012]]) ⇒ [[Ashequl Qadir]], and [[Ellen Riloff]]. ([[2012]]). “[http://aclweb.org/anthology//S/S12/S12-1028.pdf Ensemble-based Semantic Lexicon Induction for Semantic Tagging].” In: [[Proceedings of the First Joint Conference on Lexical and Computational Semantics]] ([[*Sem 2012]]). | |||
** QUOTE: [[2012_EnsemblebasedSemanticLexiconInd|We]] also evaluated the effectiveness of the [[lexicon induction task|induced]] [[lexicon]]s with respect to [[Dictionary-based Entity Mention Recognition Algorithm|instance-based semantic tagging]]. Our goal was to determine how useful the dictionaries are in two respects: (1) do the lexicons contain words that appear frequently in the domain, and (2) is dictionary look-up sufficient for instance-based labeling? Our bootstrapping processes enforce a constraint that a word can only belong to one semantic class, so if polysemy is common, then dictionary look-up will be problematic.<ref>15Only coarse polysemy across semantic classes is an issue (e.g., “plant” as a living thing vs. a factory).</ref> <P> The [[instance-based tagging|instance-based]] [[evaluation]] assigns a [[semantic label]] to each instance of a [[head noun]]. When using a [[lexicon]], all instances of the same noun are assigned the same semantic class via dictionary look-up. | |||
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Latest revision as of 18:52, 1 August 2022
A Dictionary-based Entity Mention Recognition Algorithm is an Entity Mention Recognition Algorithm that makes use of a semantic lexicon.
References
2012
- (Qadir & Riloff, 2012) ⇒ Ashequl Qadir, and Ellen Riloff. (2012). “Ensemble-based Semantic Lexicon Induction for Semantic Tagging.” In: Proceedings of the First Joint Conference on Lexical and Computational Semantics (*Sem 2012).
- QUOTE: We also evaluated the effectiveness of the induced lexicons with respect to instance-based semantic tagging. Our goal was to determine how useful the dictionaries are in two respects: (1) do the lexicons contain words that appear frequently in the domain, and (2) is dictionary look-up sufficient for instance-based labeling? Our bootstrapping processes enforce a constraint that a word can only belong to one semantic class, so if polysemy is common, then dictionary look-up will be problematic.[1]
The instance-based evaluation assigns a semantic label to each instance of a head noun. When using a lexicon, all instances of the same noun are assigned the same semantic class via dictionary look-up.
- QUOTE: We also evaluated the effectiveness of the induced lexicons with respect to instance-based semantic tagging. Our goal was to determine how useful the dictionaries are in two respects: (1) do the lexicons contain words that appear frequently in the domain, and (2) is dictionary look-up sufficient for instance-based labeling? Our bootstrapping processes enforce a constraint that a word can only belong to one semantic class, so if polysemy is common, then dictionary look-up will be problematic.[1]
- ↑ 15Only coarse polysemy across semantic classes is an issue (e.g., “plant” as a living thing vs. a factory).