Dictionary-based Entity Mention Recognition Algorithm
A Dictionary-based Entity Mention Recognition Algorithm is an Entity Mention Recognition Algorithm that makes use of a semantic lexicon.
- AKA: Instance-based Semantic Tagging.
- See: [[]].
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).