Analysis of MWES in Hindi Text Using NLTK

Joon, Rakhi; Singhal, Archana

doi:10.5121/ijnlc.2017.6102

Cited by 3 publications

(1 citation statement)

References 6 publications

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“…To begin, the expressions must affirm or detect a distinguishable and suitable condition of MWEs. The fact is that MWE-exhibiting word combinations are primarily represented by space or delimitation suggesting non-compositionality [28].…”

Section: Hindi Bengali and Telugu Multilingual Transformer Modelsmentioning

confidence: 99%

Identifying and Analyzing Reduplication Multiword Expressions in Hindi Text Using Machine Learning

Mishra,

Mishra

2023

TEM Journal

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The task of identifying and analyzing Reduplication Multiword Expressions (RMWEs) in Natural Language Processing (NLP) involves extracting repeated words from various text forms and classifying them into Onomatopoeic, non-Onomatopoeic, partial, or semantic types. With the increasing use of low-resource languages in news, opinions, comments, hashtags, reviews, posts, and journals, this study proposes a machine learning-based RMWE identification method for Hindi text. The method employs linguistic patterns and statistical data, along with a proposed threshold boundary detection in statistical filtering. The Jaccard distance of dissimilarity and Sorensen Dice Coefficient of Similarity are used for semantic relation analysis. The proposed approach was evaluated using the publicly available Hindi corpus from IITB, measuring performance between two consecutive thresholds with the lowest error and highest recall. This study proposes an effective method for Indian computational linguistics, with experimental results highlighting its viability and utility, and providing a blueprint for current procedures.

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Section: Hindi Bengali and Telugu Multilingual Transformer Modelsmentioning

confidence: 99%

Identifying and Analyzing Reduplication Multiword Expressions in Hindi Text Using Machine Learning

Mishra,

Mishra

2023

TEM Journal

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Role of Lexical and Syntactic Fixedness in Acquisition of Hindi MWEs

Joon

Singhal

2019

Communications in Computer and Information Science

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Influence of GUJarati STEmmeR in Supervised Learning of Web Page Categorization

Patel¹,

Patel²

2021

IJISA

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With the large quantity of information offered on-line, it's equally essential to retrieve correct information for a user query. A large amount of data is available in digital form in multiple languages. The various approaches want to increase the effectiveness of on-line information retrieval but the standard approach tries to retrieve information for a user query is to go looking at the documents within the corpus as a word by word for the given query. This approach is incredibly time intensive and it's going to miss several connected documents that are equally important. So, to avoid these issues, stemming has been extensively utilized in numerous Information Retrieval Systems (IRS) to extend the retrieval accuracy of all languages. These papers go through the problem of stemming with Web Page Categorization on Gujarati language which basically derived the stem words using GUJSTER algorithms [1]. The GUJSTER algorithm is based on morphological rules which is used to derived root or stem word from inflected words of the same class. In particular, we consider the influence of extracted a stem or root word, to check the integrity of the web page classification using supervised machine learning algorithms. This research work is intended to focus on the analysis of Web Page Categorization (WPC) of Gujarati language and concentrate on a research problem to do verify the influence of a stemming algorithm in a WPC application for the Gujarati language with improved accuracy between from 63% to 98% through Machine Learning supervised models with standard ratio 80% as training and 20% as testing.

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Analysis of MWES in Hindi Text Using NLTK

Abstract: Natural Language Toolkit (NLTK) is a generic platform to process the data of various natural (human) languages and it provides various resources for

Cited by 3 publications

References 6 publications

Identifying and Analyzing Reduplication Multiword Expressions in Hindi Text Using Machine Learning

Identifying and Analyzing Reduplication Multiword Expressions in Hindi Text Using Machine Learning

Role of Lexical and Syntactic Fixedness in Acquisition of Hindi MWEs

Influence of GUJarati STEmmeR in Supervised Learning of Web Page Categorization

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