Name
Abstract | ||
|---|---|---|
We present an implementation of a part-of-speech tagger based on a hidden Markov model. The methodology enables robust and accurate tagging with few resource requirements. Only a lexicon and some unlabeled training text are required. Accuracy exceeds 96%. We describe implementation strategies and optimizations which result in high-speed operation. Three applications for tagging are described: phrase recognition; word sense disambiguation; and grammatical function assignment. |
Year | Venue | Keywords |
|---|---|---|
1992 | Applied Natural Language Processing Conference | word sense disambiguation,part-of-speech tagger,resource requirement,phrase recognition,high-speed operation,hidden markov model,accurate tagging,practical part-of-speech tagger,grammatical function assignment,implementation strategy,unlabeled training text,part of speech |
Field | DocType | Citations |
Trigram tagger,Computer science,Phrase,Speech recognition,Part of speech,Lexicon,Natural language processing,Artificial intelligence,Hidden Markov model,Word-sense disambiguation | Conference | 246 |
PageRank | References | Authors |
171.15 | 7 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Doug Cutting | 1 | 281 | 174.07 |
| Julian Kupiec | 2 | 1061 | 381.10 |
| Jan O. Pedersen | 3 | 6301 | 1177.07 |
| Penelope Sibun | 4 | 284 | 187.65 |