A Scientometric Inspection of Research Based on WordNet Lexical During 1995–2019

Jain, Minni; Sharma, Gaurav; Jain, Amita

doi:10.1007/978-981-16-1502-3_10

Cited by 1 publication

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“…Fuzzy Graph connectivity measures (FGCM) were proposed by Jain and Lobiyal (2015a, 2015b). It has been heavily used in wide applications of NLP for instance (Jain et al, 2019; Jain et al, 2022; Jain, Sharma, et al, 2021; Vij et al, 2017; Vij et al, 2019). FGCM help in finding the vitality of a particular node.…”

Section: Preliminariesmentioning

confidence: 99%

Code‐mixed Hindi‐English text correction using fuzzy graph and word embedding

2023

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Interaction via social media involves frequent code‐mixed text, spelling errors and noisy elements, which creates a bottleneck in the performance of natural language processing applications. This proposed work is the first approach for code‐mixed Hindi‐English social media text that comprises language identification, detection and correction of non‐word (Out of Vocabulary) errors as well as real‐word errors occurring simultaneously. Each identified language (Devanagari Hindi, Roman Hindi, and English) has its own complexities and challenges. Errors are detected individually for each language and a suggestive list of the erroneous words is created. After this, a fuzzy graph between different words of the suggestive lists is generated using various semantic relations in Hindi WordNet. Word embeddings and Fuzzy graph‐based centrality measures are used to find the correct word. Several experiments are performed on different social media datasets taken from Instagram, Twitter, YouTube comments, Blogs, and WhatsApp. The experimental results demonstrate that the proposed system corrects out‐of‐vocabulary words as well as real‐word errors with a maximum recall of 0.90 and 0.67, respectively for Dev_Hindi and 0.87 and 0.66, respectively for Rom_Hindi. The proposed method is also applied for state‐of‐art sentiment analysis approaches where the F1‐score has been visibly improved.

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