Stochastic Part-of-Speech Tagging System

A Stochastic Part-of-Speech Tagging System is a Part-of-Speech Tagging System that uses a statistical model to capture lexical and contextual information.

• AKA: Stochastic POS Tagging System, Stochastic Tagger.
• Example(s):
  • a Markov-based POS Tagger such that proposed in Church (1988),
  • …
• Counter-Example(s):
  • a Rule-based Part-of-Speech Tagging System,
  • a Phrasal Chunking System,
  • a Word Mention Tagging System,
  • a Co-Reference Resolution System,
  • a Chunking System.
• See: Natural Language Processing System, Word Sense Disambiguation System, Noun, Verb, Pronoun, Adjective, Morphology, Syntax, Lexicon.

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References

1992

• (Brill, 1992) ⇒ Eric D. Brill. (1992). “A Simple Rule-based Part of Speech Tagger.” In: Proceedings of the Conference on Applied Natural Language Processing (ANLP 1992).
  • QUOTE: Stochastic taggers have obtained a high degree of accuracy without performing any syntactic analysis on the input. These stochastic part of speech taggers make use of a Markov model which captures lexical and contextual information. The parameters of the model can be estimated from tagged [Church 88, DeRose 88, Deroualt and Merialdo 86, Garside et al. 87, Meteer et al. 91] or untagged [Cutting et al. 92, Jelinek 85, Kupiec 89] text. Once the parameters of the model are estimated, a sentence can then be automatically tagged by assigning it the tag sequence which is assigned the highest probability by the model. Performance is often enhanced with the aid of various higher level pre- and postprocessing procedures or by manually tuning the model.

1988

• (Church, 1988) ⇒ Kenneth Ward Church (1988). "A Stochastic Parts Program and Noun Phrase Parser for Unrestricted Text". In: Proceedings of the Second Conference on Applied Natural Language Processing (ACL) 136-143.

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