Effect of GloVe, Word2Vec and FastText Embedding on English and Hindi Neural Machine Translation Systems

Sitender,; Sangeeta, .; Sushma, N. Sudha; Sharma, Saksham Kumar

doi:10.1007/978-981-19-7615-5_37

Cited by 2 publications

(1 citation statement)

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“…These techniques are based on the hypothesis that words in similar contexts tend to have similar meanings. To this end, word embedding models such as Word2Vec, GloVe and FastText learn to assign similar vectors to words that have similar meanings or are used in similar contexts [32,33]. This means that words with similar semantic or syntactic properties have similar vector representations, and their embedding space distances reflect their relationships [34].…”

Section: Text Representationmentioning

confidence: 99%

A New Method for Graph-Based Representation of Text in Natural Language Processing

2023

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Natural language processing is still an emerging field in machine learning. Access to more and more data sets in textual form, new applications for artificial intelligence and the need for simple communication with operating systems all simultaneously affect the importance of natural language processing in evolving artificial intelligence. Traditional methods of textual representation, such as Bag-of-Words, have some limitations that result from the lack of consideration of semantics and dependencies between words. Therefore, we propose a new approach based on graph representations, which takes into account both local context and global relationships between words, allowing for a more expressive textual representation. The aim of the paper is to examine the possibility of using graph representations in natural language processing and to demonstrate their use in text classification. An innovative element of the proposed approach is the use of common cliques in graphs representing documents to create a feature vector. Experiments confirm that the proposed approach can improve classification efficiency. The use of a new text representation method to predict book categories based on the analysis of its content resulted in accuracy, precision, recall and an F1-score of over 90%. Moving from traditional approaches to a graph-based approach could make a big difference in natural language processing and text analysis and could open up new opportunities in the field.

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Section: Text Representationmentioning

confidence: 99%