A survey on phrase structure learning methods for text classification

TL;DR

This survey reviews various phrase structure learning methods for text classification, emphasizing their importance in capturing non-local patterns to improve classification accuracy across multiple NLP tasks.

Contribution

It provides a comprehensive comparison and analysis of phrase structure learning techniques, facilitating future research in NLP applications that utilize syntactic information.

Findings

Phrase structure learning enhances text classification performance.

Different methods vary in accuracy and computational complexity.

The survey highlights promising directions for future NLP research.

Abstract

Text classification is a task of automatic classification of text into one of the predefined categories. The problem of text classification has been widely studied in different communities like natural language processing, data mining and information retrieval. Text classification is an important constituent in many information management tasks like topic identification, spam filtering, email routing, language identification, genre classification, readability assessment etc. The performance of text classification improves notably when phrase patterns are used. The use of phrase patterns helps in capturing non-local behaviours and thus helps in the improvement of text classification task. Phrase structure extraction is the first step to continue with the phrase pattern identification. In this survey, detailed study of phrase structure learning methods have been carried out. This will enable future work in several NLP tasks, which uses syntactic information from phrase structure like grammar checkers, question answering, information extraction, machine translation, text classification. The paper also provides different levels of classification and detailed comparison of the phrase structure learning methods.