Role of Lexical and Syntactic Fixedness in Acquisition of Hindi MWEs

Joon, Rakhi; Singhal, Archana

doi:10.1007/978-981-13-9942-8_15

Cited by 2 publications

(3 citation statements)

References 13 publications

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“…It is necessary to first separate the raw corpus into n-grams, then to filter them using Dice's coefficient and PMI method to calculate association scores, and finally to determine context similarity using the Latent Semantic Analysis method in the third stage of this three-phase procedure. A baseline and statistical analysis proposed by Joon and Singhal (2019) [18]compares the extraction of Hindi MWE with that of other languages. They included precision, recall, and f-measure in the baseline.…”

Section: Literature Reviewmentioning

confidence: 99%

Identification and extraction of multiword expressions from Hindi & Urdu language in natural language processing

2022

IJATEE

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Text can be translated from one language to another using statistical machine translation, but there are still gaps in the translations because of a lack of language resource material. Building a linguistic corpus necessarily requires the extraction of multiword expressions (MWE). MWE is a collection of words with idiomatic expression properties. However, due to its non-compositional meaning of distinctive words, identifying and extracting MWE is a timeconsuming task. In this case, an automated system has been developed for the extraction of MWEs from Hindi and Urdu language sources automatically. The entire process includes tagging, pattern matching, an identification algorithm, and the extraction of MWEs from the data. Tagging each word with a unique part of speech tag is used as an input to the pattern-matching algorithm. Using pattern matching, MWE tags of specific patterns were selected, and the algorithm for automatic MWE detection was built on top of that. The conditional random field (CRF++) model was used to automatically extract the MWEs from data. Confusion matrix was used to conduct the automated evaluation of thisproposed system. For Hindi and Urdu, the calculated overall accuracy is 96.82% and 96.62%, respectively.

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Section: Literature Reviewmentioning

confidence: 99%

Identification and extraction of multiword expressions from Hindi & Urdu language in natural language processing

2022

IJATEE

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“…The word alignment methods were discussed by the authors in [20]. The statistical methods were discussed by authors in [1] and some new methods were discussed in [29] and [30]. The hybrid methods were explained in [4,11].…”

Section: Related Workmentioning

confidence: 99%

“…The authors have evaluated various measures for Hindi dataset [29,30] and the best statistical measures for various categories have been identified. In the proposed work, the MWEs extraction is done for the English Movie review dataset.…”

Section: Related Workmentioning

confidence: 99%

Statistics based evaluation of English Multi-Word Expressions

Joon,

Singhal

2019

IJEAT

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The linguistic and statistical information extraction is an important aspect of text processing. The extraction of Multi Word Expression (MWEs) plays a key role in text processing as these are used to find correct meaning of a text phrase. MWEs are the lexical phrases consisting of two or more words conveying some different meaning together other than its constituent words. The linguistics in MWEs extraction is mainly related to the text information including the Part of Speech (POS) tags, grammar rules, related literature, and so on. It is important to extract the correct MWEs for a particular language as there exists variety and veracity in languages. The selection of MWEs are based on the statistical analysis of the MWEs extraction process. In the proposed work, the MWEs extraction is done for English dataset. Along with the existing statistical measures, i.e. Pointwise Mutual Information (PMI), Dice Coefficient (DC) and Modified Dice Coefficient (MDC), the additional measures, Lexical Fixedness (LF), Syntactic Fixedness (SF) and Relevance Measure (RM) are also been evaluated. The results are compared with the other existing approaches applied for English MWEs. The results shows that the proposed measures LF, SF and RM are more significant than existing measures to find the best statistics for the MWEs extraction process. The process model is generic in nature and not adhered to a particular language. It can also be applied for other languages by selecting POS tags for that particular language.

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Role of Lexical and Syntactic Fixedness in Acquisition of Hindi MWEs

Cited by 2 publications

References 13 publications

Identification and extraction of multiword expressions from Hindi & Urdu language in natural language processing

Identification and extraction of multiword expressions from Hindi & Urdu language in natural language processing

Statistics based evaluation of English Multi-Word Expressions

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