Grammatical analysis by computer of the Lancaster-Oslo/Bergen (LOB) corpus of British English texts

David, Andrew

doi:10.3115/981210.981246

Cited by 4 publications

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“…Furthermore, some studies consider the impact of supervision and knowledge on WSD (Beale, 1985;Nirenburg & Raskin, 2001;Pilehvar & Navigli, 2014).…”

Section: Word Sense Disambiguationmentioning

confidence: 99%

“…Statistical methods have been applied widely since then. Hidden Markov model (HMM) and Viterbi algorithm have played an important role that line of research (Beale, ; Collins, ; Derose, ; Knoll, Melton, Liu, Xu, & Pakhomov, ; Pla & Molina, ; Viterbi, ). Statistical methods annotate words, employing the tags of their neighbours and the frequency of sequences.…”

Section: Related Workmentioning

confidence: 99%

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FNLP‐ONT: A feasible ontology for improving NLP tasks in Persian

2018

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Natural language processing is a composition of several error‐prone and challenging tasks, including part of speech tagging, word sense disambiguation, named entity recognition, and compound verb detection. Studying intrasentence relations and roles is essential to improve the mentioned subtasks. Semi‐automatic schemes such as ontologies can be applied to clarify word's dependencies. This paper presents an ontology that is targeting to improve POS tagging, WSD, NER, and compound verb detection in Persian with extra properties that may ameliorate machine translation. The ontology is tested in combinations with several state‐of‐art algorithms on Dadegan corpus. The results show that coping semantic analysis with machine learning methods enhance relation detection and consequently precision of the mentioned subtasks, which is not widely addressed in Persian. Furthermore, the experimental results declare that the accuracy rate increases between 4.5 and 23% for different tasks.

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“…Furthermore, some studies consider the impact of supervision and knowledge on WSD (Beale, 1985;Nirenburg & Raskin, 2001;Pilehvar & Navigli, 2014).…”

Section: Word Sense Disambiguationmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

FNLP‐ONT: A feasible ontology for improving NLP tasks in Persian

2018

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confidence: 99%

An analysis of probabilistic grammatical tagging methods

DeRose¹

1991

English Computer Corpora

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The probabilistic tagging method introduced in the CLAWS system (see for example Leech -Garside -Atwell 1983, Beale 1985, GarsideLeech 1985, 1987 has proven highly accurate in assigning the correct grammatical category labels or tags to natural language text in the Lancaster-Oslo/Bergen (LOB) Corpus. Very briefly, this method involves assigning probabilities to alternative sequenes of tag assignments, based upon (a) the collocational probabilities of adjacent hypothesized tags, p(t(n + l)|t(n)), and upon (b) the relative tag probabilities, of tags for each word, p(t(n)|w). For example, in the time, context being equal, time would be judged a noun because (a) articles are far more likely to precede nouns than verbs, and (b) time occurs over 1,000 times more frequently as a noun than as a verb.Handling such grammatical category ambiguity is an important problem for natural language processing systems, because it is so widespread. Examining the Brown Corpus gives an idea of just how widespread: about 11% of word types, and 48% of word tokens, occur with more than one category label. The actual extent of categorial ambiguity in English is certainly much higher for several reasons (e. g., the many hapax legomena, which appear to be unambiguous even though a larger corpus or native speaker intuitions may show otherwise).Similar tagging methods have been reported by DeRose (1985DeRose ( , 1988) and by Church (1988), but using dynamic programming methods to achieve linear time and space bounds. These methods also differ in that they forego detailed morphological analysis and idiom processing; Church's method adds a table of third-order transitional probabilities. Results have been generally comparable to those reported for CLAWS.In this article, I report on several investigations I carried out in order to evaluate this class of methods, and the relative effects of certain variations. This work led to my dissertation, Stochastic methods for resolution of grammatical category ambiguity in inflected and uninflected languages, completed in 1989 at Brown University (DeRose 1989). 1

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