MaTop: An Evaluative Topic Model for Marathi

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3AUAXPW2ZM" target="_blank" >RIV/00216208:11320/22:UAXPW2ZM - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1007/978-981-16-4538-9_14" target="_blank" >https://doi.org/10.1007/978-981-16-4538-9_14</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-981-16-4538-9_14" target="_blank" >10.1007/978-981-16-4538-9_14</a>

Jazyk výsledku

angličtina

Název v původním jazyce

MaTop: An Evaluative Topic Model for Marathi

Popis výsledku v původním jazyce

Topic modeling is a text mining technique that presents the theme of the corpus by identifying latent features of the language. It thus provides contextual information of the documents in the form of topics and their representative words, thereby reducing time, efforts, etc. Topic modeling on English corpus is a common task, but topic modeling on regional languages like Marathi is not explored yet. The proposed approach implements a topic model on Marathi corpus containing more than 1200 documents. Intrinsic evaluation of latent Dirichlet allocation (LDA) which is used to implement the topic model is carried out by coherence measure. Its value is maximum for 4 topics. The retrieved topics are related to ‘Akbar–Birbal,’ ‘Animal stories,’ ‘Advise giving stories’ and ‘general stories.’ Dendrogram and word cloud are used for visualization. The dendrogram shows topic-wise documents and word cloud show sample informative words from different stories. The proposed approach involves context while deriving the topics using synsets. Entropy value is 1.5 for varied datasets; entropy value ensures independence of topic and similarity between topics’ words.

Název v anglickém jazyce

MaTop: An Evaluative Topic Model for Marathi

Popis výsledku anglicky

Topic modeling is a text mining technique that presents the theme of the corpus by identifying latent features of the language. It thus provides contextual information of the documents in the form of topics and their representative words, thereby reducing time, efforts, etc. Topic modeling on English corpus is a common task, but topic modeling on regional languages like Marathi is not explored yet. The proposed approach implements a topic model on Marathi corpus containing more than 1200 documents. Intrinsic evaluation of latent Dirichlet allocation (LDA) which is used to implement the topic model is carried out by coherence measure. Its value is maximum for 4 topics. The retrieved topics are related to ‘Akbar–Birbal,’ ‘Animal stories,’ ‘Advise giving stories’ and ‘general stories.’ Dendrogram and word cloud are used for visualization. The dendrogram shows topic-wise documents and word cloud show sample informative words from different stories. The proposed approach involves context while deriving the topics using synsets. Entropy value is 1.5 for varied datasets; entropy value ensures independence of topic and similarity between topics’ words.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

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

Projekt

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Návaznosti

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Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of Third International Conference on Sustainable Computing

ISBN

978-981-16-4538-9

ISSN

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e-ISSN

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Počet stran výsledku

10

Strana od-do

135-144

Název nakladatele

Springer Nature

Místo vydání

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Místo konání akce

Singapore

Datum konání akce

1. 1. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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