Word Mention to Word Sense Resolution Task
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A Word Mention to Word Sense Resolution Task is a reference resolution task that requires the mapping of each Word Mention to another Word Sense Referencer with the same Referent.
- AKA: Word Mention Reference Resolution Task, Word Sense Normalization Task, Word Sense Resolution Task, Word Mention to Word Sense Mapping Task, Word Mention Normalization, Word Mention to Concept Record Resolution Task, WMNT, Mention Reference Resolution Task, Word Mention Resolution Task, Word Reference Resolution Task, Word Mention Reference Resolution.
- Context:
- Input: a Text String/Text Artifact (e.g. produced by a Word Mention Detection Task)
- Optional Input:
- Output: a Sense Tagged Expression (with a Word Sense Record Identifier for some or all Word Mention whose sense is represented)
- Optional Output.
- It can be:
- a Word Sense Discrimination Task (where a Word Sense Inventory is NOT provided).
- a Word Sense Superclass Classification Task (where a Semantic Class Inventory is provided) such as a Named Entity Recognition Task.
- a Word Sense Classification Task (where a Word Sense Inventory is provided).
- It can be:
- It can be:
- an All-Words Word Sense Resolution Task (for all the words in the input)
- a Target-Word Word Sense Resolution Task (for some specified words in the input).
- It can be:
- It can be solved by a Word Mention Reference Resolution System that implements a Word Mention Reference Resolution Algorithm.
- It can be supported by a Word Mention Detection Task.
- It can assume that all Word Mentions have been accurately detected.
- It can support a Semantic Annotation Task.
- Example(s):
- a Word Sense Disambiguation Task, if the Word Mention is for a Dictionary Word in a Dictionary (which assumes that the Word Sense Record is present).
- a Named Entity Mention Reference Resolution Task/Entity Mention Normalization Task, if the Word Sense Inventorys is restricted to an Entity Database.
- a Term Mention Reference Resolution Task, if the Word Mentions are restricted to Term Mentions, such as:
- \(f\)("The system supports tree-structured conditional random field models.") ⇒ "The system supports tree-structured CRF models.
- Counter-Example(s):
- See: DNA Segment Reference Resolution Task.
References
2011
- (Mihalcea, 2011) ⇒ Rada Mihalcea. (2011). "Word Sense Disambiguation" In: (Sammut & Webb, 2011) p.1027
2009
- (Navigli, 2009) ⇒ Roberto Navigli. (2009). "Word Sense Disambiguation: A survey." In: ACM Computing Surveys (CSUR) 41(2). doi:10.1145/1459352.1459355
2002
- (Pantel & Lin, 2002b) ⇒ Patrick Pantel, and Dekang Lin. (2002). "Discovering Word Senses from Text." In: Proceedings of the eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2002). doi:10.1145/775047.775138
1998
- (Schütze, 1998) ⇒ Hinrich Schütze. (1998). "Automatic Word Sense Discrimination." In: Computational Linguistics, 24(1).
1995
- (Yarowsky, 1995) ⇒ David Yarowsky. (1995). "Unsupervised Word Sense Disambiguation Rivaling Supervised Methods." Proceedings of the 33rd annual meeting on Association for Computational Linguistics. http://dx.doi.org/10.3115/981658.981684
1994
- (Grefenstette, 1994) ⇒ Gregory Grefenstette. (1994). "Explorations in Automatic Thesaurus Discovery." Kluwer, ISBN 0792394682
1993
- (Leacock & al, 1993) ⇒ Claudia Leacock, Geoffrey Towell, and Ellen Voorhees. (1993). "Corpus-based Statistical Sense Resolution." In: Proceedings of the workshop on Human Language Technology at HLT 1993.
1986
- (Lesk, 1986) ⇒ Michael Lesk. (1986). "Automatic Sense Disambiguation Uusing Machine Readable Dictionaries: How to tell a pine cone from a ice cream cone." In: Proceedings of SIGDOC-1986.
