Difference between revisions of "Part-of-speech tagging"
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After morphological analysis of a sentence, a not insignificant amount of words will have more than one analysis. For example in the following sentence: |
After morphological analysis of a sentence, a not insignificant amount of words will have more than one analysis. For example in the following sentence: |
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| − | :Vino (noun or verb) a ( la playa |
+ | :Vino (<code>noun</code> or <code>verb</code>) a (<code>preposition</code>) la (<code>determiner</code> or <code>pronoun</code>) playa (<code>noun</code>) |
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Revision as of 08:28, 16 September 2008
Part-of-speech tagging is the process of assigning unambiguous grammatical categories[1] to words in context. The crux of the problem is that surface forms of words can often be assigned more than one part-of-speech by morphological analysis. For example in English, the word "trap" can be both a singular noun ("a trap") or a verb ("I'll trap it").
This page intends to give an overview of how part-of-speech tagging works in Apertium, primarily within the apertium-tagger, but giving a short overview of constraints (as in constraint grammar) and restrictions (as in apertium-tagger) as well.
Lexical ambiguity
After morphological analysis of a sentence, a not insignificant amount of words will have more than one analysis. For example in the following sentence:
- Vino (
nounorverb) a (preposition) la (determinerorpronoun) playa (noun)
Hidden Markov models
A hidden Markov model is a statistical model which consists of a number of hidden states, and a number of observable states.
Ambiguity classes
Training
Expectation-Maximisation (EM)
Baum-Welch
Tagging
Viterbi
See also
Notes
- ↑ Also referred to as "parts-of-speech", e.g. Noun, Verb, Adjective, Adverb, Conjunction, etc.
