CURRENT TRENDS IN THE RECOGNITION AND DECODING OF PHRASEOLOGICAL UNITS
Iryna BASARABA, Ірина Олександрівна Бец, Yurii Bets · Humanities science current issues · 2024
The article defines that one of the key problems in natural language processing is the recognition of phraseological units.The authors argue that automatic phrase recognition is a rather complex task that requires a combination of linguistic knowledge, machine learning algorithms, and computer technology.Rule-based approaches are one of the most common methods for identifying and categorizing phraseological units in natural language processing.These methods operate on a set of predefined rules to detect and extract phrases from text using syntactic and semantic patterns.The article notes that one of the main advantages of machine learning methods in phrase detection is their ability to effectively cope with the complexity and variability of natural language.According to the authors, rule-based approaches to phrase recognition are mostly implemented in the form of software that can be integrated into various NLP tools and platforms, such as: Natural Language Toolkit (NLTK); Stanford CoreNLP, where CoreNLP is a set of NLP tools; Apache OpenNLP: OpenNLP (an open source NLP library); GATE: General Architecture for Text Engineering (GATE) (an open-source platform for building NLP applications); spaCy: (a Python-based NLP library that includes a rule-based search engine for identifying phrases in text).The authors identify different types of rule-based approaches that can be used for phrase recognition, including pattern matching, rule induction, and decision trees.However, one of the main challenges is the creation of a comprehensive set of rules that can accurately identify and classify all possible phraseological units in a given language, which can be particularly difficult for languages with complex grammatical structures or large vocabularies.The authors argue that a significant advantage of machine learning methods in phrase detection is their ability to effectively cope with the complexity and variability of natural language.The paper identifies the basic principles that guide how machine learning approaches work for phrase recognition.