Statistical analysis of Hindi multi-word expressions using multiple threshold method

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F26%3AS3CRRDDV" target="_blank" >RIV/00216208:11320/26:S3CRRDDV - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1007/s44163-025-00291-z" target="_blank" >http://dx.doi.org/10.1007/s44163-025-00291-z</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s44163-025-00291-z" target="_blank" >10.1007/s44163-025-00291-z</a>

Result language

angličtina

Original language name

Statistical analysis of Hindi multi-word expressions using multiple threshold method

Original language description

Multiword Expressions (MWEs) extraction is one of the important aspects of text processing, which is used to find the correct meaning of a text phrase. MWEs are lexical phrases consisting of two or more words exhibiting semantic property. MWEs play a vital role in many Natural Language Processing (NLP) applications like machine translation, information retrieval, text processing, and other practical applications. Much of the research in this area has focused on the extraction and analysis of MWEs in English and other natural languages. The MWEs in Hindi have not gained much attention from earlier researchers. In the proposed work the statistical aspects of Hindi MWEs are explored using various statistical measures. Different classes of functional classification of Hindi MWEs are considered for the statistical analysis and experiments. This paper mainly focuses on the evaluation of the following statistical measures, Pointwise Mutual Information (PMI), Dice Coefficient (DC), Modified Dice Coefficient (MDC), Lexical Fixedness (LF), Syntactic Fixedness (SF), and Relevance Measure (RM). The dataset used for the evaluation of MWEs is the benchmark dataset collected from Hindi novels written by “Munshi Premchand Ji”. The best statistical measures are also identified for each functional category of Hindi MWEs. Different threshold values have been obtained for the evaluation of the functional categories. The threshold value represents the maximum limit of the corpus size that one can select for efficient evaluation of a specific category of Hindi MWEs. This approach has been applied to two different datasets to compare and justify the obtained results. © The Author(s) 2025.

Czech name

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Czech description

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Type

J_SC - Article in a specialist periodical, which is included in the SCOPUS database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

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Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Discover Artificial Intelligence

ISSN

2731-0809

e-ISSN

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Volume of the periodical

5

Issue of the periodical within the volume

1

Country of publishing house

US - UNITED STATES

Number of pages

27

Pages from-to

46

UT code for WoS article

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EID of the result in the Scopus database

2-s2.0-105004425390