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Part-of-speech tagging

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Part-of-speech tagging basics and techniques
– Part-of-speech tagging is the process of marking up a word in a text as corresponding to a particular part of speech.
– It is based on both the definition and context of the word.
– POS tagging is commonly taught to school-age children to identify words as nouns, verbs, adjectives, adverbs, etc.
– POS tagging is now done using algorithms in computational linguistics.
– There are two groups of POS-tagging algorithms: rule-based and stochastic.
– Adjective and number percentages can help determine the part of speech.
– More advanced HMMs can learn probabilities of larger sequences.
– Enumerating every combination and assigning relative probabilities can improve accuracy.
– CLAWS achieved 93-95% accuracy in part-of-speech tagging.
– Charniak’s research showed that assigning the most common tag to known words and ‘proper noun’ to unknowns can achieve 90% accuracy.
– DeRose and Church developed dynamic programming algorithms for part-of-speech tagging.
– DeRose used a table of pairs, while Church used a table of triples.
– Both methods achieved over 95% accuracy.
– DeRose’s work was replicated for Greek and proved effective.
– Unsupervised tagging techniques use untagged corpora to derive part-of-speech categories.
– Iterative processes reveal patterns in word use and similarity classes.
– Rule-based, stochastic, and neural approaches are used in unsupervised tagging.
– Unsupervised tagging can provide valuable new insights.
– Induction-based methods can achieve accuracy above 95%.
– Major algorithms for part-of-speech tagging include the Viterbi algorithm, Brill tagger, Constraint Grammar, and Baum-Welch algorithm.
– Hidden Markov model and visible Markov model taggers use the Viterbi algorithm.
– The rule-based Brill tagger applies learned rule patterns.
– Machine learning methods like SVM, maximum entropy classifier, perceptron, and nearest-neighbor have been applied to part-of-speech tagging.
– A direct comparison of methods reported 97.36% accuracy using the structure regularization method.

Tag Sets and Variations
– English has 9 commonly taught parts of speech, but there are many more categories and sub-categories.
– Nouns can have plural, possessive, and singular forms, while verbs can be marked for tense and aspect.
– Different inflections of the same root word can have different parts of speech.
– Tag sets for POS tagging in English can range from 50 to 150 separate parts of speech.
– Different languages have different tag sets, with heavily inflected languages having larger tag sets.

History and Development
– Research on part-of-speech tagging has been closely tied to corpus linguistics.
– The Brown Corpus, developed in the mid-1960s, was the first major corpus of English for computer analysis.
– The Brown Corpus was painstakingly tagged with part-of-speech markers over many years.
– The corpus has been used for numerous studies and inspired the development of similar tagged corpora in other languages.
– Part-of-speech tagging was considered an inseparable part of natural language processing for a long time.
– The Brown Corpus consists of about 1,000,000 words of running English prose text.
– It was tagged with part-of-speech markers using a program and later reviewed and corrected by hand.
– The tagging of the Brown Corpus formed the basis for many later part-of-speech tagging systems.
– Larger corpora, such as the 100 million word British National Corpus, have since superseded the Brown Corpus.
– Part-of-speech tagging was considered essential in natural language processing due to the ambiguity of certain words.
– In the mid-1980s, researchers began using hidden Markov models (HMMs) to disambiguate parts of speech.
– HMMs involve counting cases and creating a table of probabilities for certain word sequences.
– HMMs were used to tag the Lancaster-Oslo-Bergen Corpus of British English.
– The use of HMMs improved part-of-speech tagging accuracy.
– HMMs reduced the need for analyzing higher levels of language understanding for each word.

Unsupervised Tagging
– Unsupervised tagging techniques use untagged corpora to derive part-of-speech categories.
– Iterative processes reveal patterns in word use and similarity classes.
– Rule-based, stochastic, and neural approaches are used in unsupervised tagging.
– Unsupervised tagging can provide valuable new insights.
– Induction-based methods can achieve accuracy above 95%.

Related Topics and References
– See also: Semantic net, sliding window based part-of-speech tagging, trigram tagger, and word sense disambiguation.
– References include POS tags in Sketch Engine and A Universal Part-of-Speech Tagset.
– Works cited: Charniak’s ‘Statistical Techniques for Natural Language Parsing’ and DeRose’s ‘Stochastic Methods for Resolution of Grammatical Category Ambiguity in Inflected and Uninflected Languages.’

In corpus linguistics, part-of-speech tagging (POS tagging or PoS tagging or POST), also called grammatical tagging is the process of marking up a word in a text (corpus) as corresponding to a particular part of speech, based on both its definition and its context. A simplified form of this is commonly taught to school-age children, in the identification of words as nouns, verbs, adjectives, adverbs, etc.

Once performed by hand, POS tagging is now done in the context of computational linguistics, using algorithms which associate discrete terms, as well as hidden parts of speech, by a set of descriptive tags. POS-tagging algorithms fall into two distinctive groups: rule-based and stochastic. E. Brill's tagger, one of the first and most widely used English POS-taggers, employs rule-based algorithms.

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