The Relationship Between AI and Linguistics

In recent years, advances in artificial intelligence (AI) have led to rapid progress in technologies for processing human language by computers. AI and linguistics may seem to be different fields at first glance, but in fact, they are closely related. In particular, in the field of Natural Language Processing (NLP), linguistic knowledge has greatly contributed to the development of AI. Linguistics is the scientific study of the structures and characteristics of human language. It consists of various fields, such as phonology, which deals with speech sounds; morphology, which deals with word structure; syntax, which deals with sentence structure; semantics, which deals with meaning; and pragmatics, which deals with language use in context. On the other hand, AI is a technology that learns from large amounts of data and performs problem-solving and prediction. In order for AI to understand and generate human language, knowledge of language structure and meaning is necessary, and therefore the findings of linguistic research are used. For example, when processing Japanese texts by computer, it is first necessary to divide sentences into words. However, unlike English, Japanese does not have spaces between words, so morphological analysis, which automatically extracts words, becomes important. In this process, knowledge of morphology in linguistics is used. In addition, syntactic knowledge is used in parsing sentence structures, and semantic ideas are used when understanding the meanings of words and sentences. Recent generative AI is based on technologies called Large Language Models (LLMs). These models learn from enormous amounts of text data and can generate human-like sentences. However, AI is not simply learning statistical patterns. In order to evaluate and improve its performance, linguistic perspectives are essential. For example, linguistic knowledge plays an important role when judging whether generated sentences are grammatically correct, semantically natural, and appropriate to the context. AI also brings new possibilities to linguistic research itself. AI can now support large-scale text analysis that researchers previously had to conduct manually. As a result, corpus analysis, language change research, and multilingual comparative studies can be carried out more efficiently. In addition, practical technologies such as machine translation, speech recognition, and dialogue systems have developed through cooperation between AI and linguistics. In this way, AI and linguistics develop while influencing each other. Linguistics provides AI with a theoretical foundation for understanding human language, while AI provides linguistics with new research methods and analytical environments. As cooperation between the two fields continues to advance, more sophisticated language understanding technologies and further development of language research can be expected.