Sliding window based part-of-speech tagging
Sliding window based part-of-speech tagging is used to part-of-speech tag a text. A high percentage of words in a natural language are words which out of context can be assigned more than one part of speech. The percentage of these ambiguous words is typically around 30%, although it depends greatly on the language. Solving this problem is very important in many areas of natural language processing. For example in machine translation changing the part-of-speech of a word can dramatically change its translation. Sliding window based part-of -speech taggers are programs which assign a single part-of-speech to a given lexical form of a word, by looking at a fixed sized "window" of words around the word to be disambiguated. The two main advantages of this approach are: Formal definition. Let be the set of grammatical tags of the application, that is, the set of all possible tags which may be assigned to a word, and let be the vocabulary of the application. Let be a function for morphological analysis which assigns each formula_4 its set of possible tags, formula_5, that can be implemented by a  full-form lexicon, or a morphological analyser. Let be the set of word classes, that in general will be a partition of formula_7 with the restriction that for each formula_8 all of the words formula_9 will receive the same set of tags, that is, all of the words in each word class formula_10 belong to the same ambiguity class. Normally, formula_11 is constructed in a way that for high frequency words, each word class contains a single word, while for low frequency words, each word class corresponds to a single ambiguity class. This allows good performance for high frequency ambiguous  words, and doesn't require too many parameters for the tagger. With these definitions it is possible to state problem in the following way: Given a text formula_12 each word formula_13 is assigned a word class formula_14 (either by using the lexicon or morphological analyser) in order to get an ambiguously tagged text formula_15. The job of the tagger is to get a tagged text formula_16 (with formula_17) as correct as possible. A statistical tagger looks for the most probable tag for an ambiguously tagged text formula_18: Using Bayes formula, this is converted into: where formula_21 is the probability that  a particular tag (syntactic probability) and formula_22 is the probability that this tag corresponds to the text formula_23 (lexical probability). In a Markov model, these probabilities are approximated as products. The syntactic probabilities are modelled by a first order Markov process: where formula_25 and formula_26 are delimiter symbols. Lexical probabilities are independent of context: One form of tagging is to approximate the first probability formula: where formula_29 is the right context of the size formula_30. In this way the sliding window algorithm only has to take into account a context of size formula_31. For most applications formula_32. For example to  tag the ambiguous word "run" in the sentence "He runs from danger", only the tags of the words "He" and "from" are needed to be taken into account.