<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This paper first researches special English vocabulary teaching defines special English vocabulary definitions and characteristics based on special English usage, and proposes strategies for learning special English vocabulary. The study involves studying the semantic recognition of special English using data corpus technology. Based on data corpus technology, the semantic structure of English vocabulary is analyzed according to Chinese analysis and word vectors, and to facilitate the extraction of English semantic features, IOB<sub>2</sub> labeled training corpus is needed. Then, the CRFs network algorithm in the data corpus technology is used to construct the special English semantic recognition model, the lexical annotation set is refined on the preprocessing, and at the same time, the semantic features are added to the recognition model of CRFs mainly used to recognize the noun phrases of gerund class and the experimental analysis is carried out on the recognition of the semantic patterns of the special English vocabulary. The results show that among high school students with high English semantic recognition ability, the correlation coefficient between their aggregates and reading level is 0.753, the correlation coefficient between collocated phrases and reading level is 0.772, and there is a high correlation between the ability to recognize aggregates, collocated phrases, and sentence framing phrases and reading level. This study is designed to improve the efficiency of English reading and communication and has a facilitatory effect on recognizing English lexical semantic patterns.</p>
</abstract>

This paper first researches special English vocabulary teaching defines special English vocabulary definitions and characteristics based on special English usage, and proposes strategies for learning special English vocabulary. The study involves studying the semantic recognition of special English using data corpus technology. Based on data corpus technology, the semantic structure of English vocabulary is analyzed according to Chinese analysis and word vectors, and to facilitate the extraction of English semantic features, IOB2 labeled training corpus is needed. Then, the CRFs network algorithm in the data corpus technology is used to construct the special English semantic recognition model, the lexical annotation set is refined on the preprocessing, and at the same time, the semantic features are added to the recognition model of CRFs mainly used to recognize the noun phrases of gerund class and the experimental analysis is carried out on the recognition of the semantic patterns of the special English vocabulary. The results show that among high school students with high English semantic recognition ability, the correlation coefficient between their aggregates and reading level is 0.753, the correlation coefficient between collocated phrases and reading level is 0.772, and there is a high correlation between the ability to recognize aggregates, collocated phrases, and sentence framing phrases and reading level. This study is designed to improve the efficiency of English reading and communication and has a facilitatory effect on recognizing English lexical semantic patterns.

Semantic pattern recognition of special English words based on data corpus technology

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

This paper first researches special English vocabulary teaching defines special English vocabulary definitions and characteristics based on special English...

Semantic pattern recognition of special English words based on data corpus technology

Online veröffentlicht: 08. Nov. 2023

Seitenbereich: -

Eingereicht: 01. Feb. 2023

Akzeptiert: 19. Mai 2023

DOI: https://doi.org/10.2478/amns.2023.2.01037

Schlüsselwörter
CRFs recognition model, Word vectors, IOB annotation, Data corpus technology, Special English vocabulary, Lexical semantics

© 2023 Qingxia He, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Semantic pattern recognition of special English words based on data corpus technology

Online veröffentlicht: 08. Nov. 2023

Seitenbereich: -

Eingereicht: 01. Feb. 2023

Akzeptiert: 19. Mai 2023

DOI: https://doi.org/10.2478/amns.2023.2.01037

SchlüsselwörterCRFs recognition model, Word vectors, IOB annotation, Data corpus technology, Special English vocabulary, Lexical semantics

© 2023 Qingxia He, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
CRFs recognition model, Word vectors, IOB annotation, Data corpus technology, Special English vocabulary, Lexical semantics