1 Introduction

Introduced the concept of word vectors, their applications in linguistic research, and their importance. Word vectors map the semantic syntactic features of words to vectors in the semantic space, enabling the comparison of semantic similarity between different language units. In recent years, word vectors have been widely used in quantitative linguistics research, especially in the fields of morphology and semantics, where significant achievements have been made. At the same time, it is pointed out that the attention and understanding of word vectors in the domestic linguistic community are not sufficient. This article aims to introduce the theoretical background and application of word vectors, explore their advantages and disadvantages, stimulate domestic research interest, and provide new perspectives and methods for quantitative linguistics research.

The basic principle of 2-word vectors

Introduced the basic principles of word vectors, including their theoretical background, models, calculation methods for semantic similarity, and dimensionality reduction techniques. The distribution hypothesis, as the theoretical basis of word vectors, emphasizes the importance of the co-occurrence relationship between word semantics and context. Word vector models are divided into static and dynamic types, with the former such as Word2Vec, GloVe, FastText, and the latter such as ELMo, BERT, GPT. The calculation of semantic similarity usually adopts methods such as cosine similarity, while dimensionality reduction techniques such as PCA, t-SNE, etc. are used to project high-dimensional word vector data into a low dimensional space, in order to more intuitively display semantic relationships.

Three word vectors and linguistic research

Word vector technology plays an important role in linguistic research, especially in the fields of morphology and lexical semantics. In terms of morphology, word vectors can capture changes in lexical form, such as representing changes in inflectional form through displacement vectors. The average displacement vector is used to study the morphological changes of nouns, such as the proximity of singular and plural nouns in semantic space. Researchers analyzed the number and case changes of nouns by calculating the average displacement vector, and found that the addition of semantic categories improved the accuracy of model predictions. In addition, word vectors are also used to analyze derivative phenomena and word formation research, such as the semantic differences between the prefixes PE - and PEN - in Indonesian, as well as the cognitive basis of Chinese word formation.

4 Discussion and Prospect

Word vectors have limitations in handling polysemous and unregistered words, and static word vectors cannot distinguish between polysemy and homophones. Supervised learning or clustering methods are needed to improve this. Chinese pre training word vectors provide vectors such as words, phrases, etc., avoiding the problem of multiple word idioms and idioms in English without independent vectors. However, the phenomenon of polysemy in Chinese is more complex. The context based dynamic word vector model can compensate for its shortcomings, but the technical threshold is high and interdisciplinary collaboration is more effective. Word vectors require a combination of linguistic theory and statistical methods for various quantitative analyses, which complement behavioral feature analysis. The word vector method has formed a cross disciplinary research between corpus linguistics and computational linguistics, expanding the research perspective. In the future, word vector methods are expected to play a greater role in word construction, word formation research, cognitive semantics, and other fields, providing new data and ideas for psycholinguistics, corpus linguistics, and more.

5 Conclusion

Deeply explored the core principles, models, and pre training databases of word vectors, especially the Chinese word vector database; Detailed display of the calculation method for semantic similarity; Summarized the latest research progress of word vectors in the fields of morphology and lexical semantics; Discussed the limitations of word vectors and their relationship with other quantitative methods; Explored future research directions; Emphasizing that word vectors are a beneficial supplement to linguistic research, rather than a replacement for existing methods; Theoretical linguistic research based on word vectors should be mutually validated with corpora and experimental data; It is pointed out that the field of linguistics in China has great potential for development in the application of word vectors, and is expected to make profound progress in multiple branches.