Embedding representation of words in sign language

IF 2.1 2区数学 Q1 MATHEMATICS, APPLIED

Journal of Computational and Applied Mathematics Pub Date : 2025-02-21 DOI:10.1016/j.cam.2025.116590

Marcin Mateusz Czajka , Daria Kubacka , Aleksandra Świetlicka

{"title":"Embedding representation of words in sign language","authors":"Marcin Mateusz Czajka , Daria Kubacka , Aleksandra Świetlicka","doi":"10.1016/j.cam.2025.116590","DOIUrl":null,"url":null,"abstract":"<div><div>Word Embedding is currently the standard in machine learning methods for natural language processing. It is a matrix that represents the interdependence between words in a given linguistic corpus. This matrix is N x dimension, where N is the number of words in a given linguistic corpus, and the dimension is most often 100 or 300. The embedding matrix mathematically represents the semantic distance between individual words. Various methods exist for generating such a matrix for natural language, such as Word2Vec or GloVe.</div><div>In this work, we want to focus on creating an embedding matrix for Polish Sign Language (PSL). Sign language has different characteristics than the spoken language; it is the so-called spatial language, encompassing not only gestures but also facial expressions and body language. As a result, it has no official written form, though signs can be represented using glosses. With a dataset of sentences annotated with glosses, we attempted the generation of an embedding matrix that could be used in further researches on translation between Polish and PSL. For this purpose, the abovementioned Word2Vec and GloVe methods, with addition of fastText, ELMo and BERT algorithms, will be employed.</div></div>","PeriodicalId":50226,"journal":{"name":"Journal of Computational and Applied Mathematics","volume":"465 ","pages":"Article 116590"},"PeriodicalIF":2.1000,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Computational and Applied Mathematics","FirstCategoryId":"100","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0377042725001050","RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATHEMATICS, APPLIED","Score":null,"Total":0}

引用次数: 0

Abstract

Word Embedding is currently the standard in machine learning methods for natural language processing. It is a matrix that represents the interdependence between words in a given linguistic corpus. This matrix is N x dimension, where N is the number of words in a given linguistic corpus, and the dimension is most often 100 or 300. The embedding matrix mathematically represents the semantic distance between individual words. Various methods exist for generating such a matrix for natural language, such as Word2Vec or GloVe.

In this work, we want to focus on creating an embedding matrix for Polish Sign Language (PSL). Sign language has different characteristics than the spoken language; it is the so-called spatial language, encompassing not only gestures but also facial expressions and body language. As a result, it has no official written form, though signs can be represented using glosses. With a dataset of sentences annotated with glosses, we attempted the generation of an embedding matrix that could be used in further researches on translation between Polish and PSL. For this purpose, the abovementioned Word2Vec and GloVe methods, with addition of fastText, ELMo and BERT algorithms, will be employed.

查看原文本刊更多论文

求助全文

约1分钟内获得全文求助全文

来源期刊

Journal of Computational and Applied Mathematics 数学-应用数学

CiteScore

5.40

自引率

4.20%

发文量

437

审稿时长

3.0 months

期刊介绍： The Journal of Computational and Applied Mathematics publishes original papers of high scientific value in all areas of computational and applied mathematics. The main interest of the Journal is in papers that describe and analyze new computational techniques for solving scientific or engineering problems. Also the improved analysis, including the effectiveness and applicability, of existing methods and algorithms is of importance. The computational efficiency (e.g. the convergence, stability, accuracy, ...) should be proved and illustrated by nontrivial numerical examples. Papers describing only variants of existing methods, without adding significant new computational properties are not of interest. The audience consists of: applied mathematicians, numerical analysts, computational scientists and engineers.