ANALYSIS OF DEEP LEARNING MODELS FOR TEXT IN-FORMATION CLASSIFICATION TASKS

Abstract

Text analysis as a whole is a new field of study. Fields such as marketing, product management, research, and management already use the process of analysing and extracting information from textual data. In the previous post, we discussed text classification technology, one of the most important parts of text analysis. Text classification or text categorisation is the activity of labelling texts in natural language with appropriate categories from a predetermined set. To put it bluntly, text classification is the process of extracting generic tags from unstructured text. These generic tags come from a set of predefined categories. Categorising content and products helps users easily find and navigate to a website or app. Text classification, also known as text categorisation, is a classic problem in natural language processing (NLP) that aims to assign labels or tags to text units such as sentences, queries, paragraphs, and documents. It has a wide range of applications, including question answering, spam detection, sentiment analysis, news categorisation, user intent classification, content moderation, and more. Text data can come from a variety of sources, including web data, emails, chats, social media, tickets, insurance claims, user feedback, and customer service questions and answers. The text is an extremely rich source of information. But extracting useful data from text is usually difficult and time-consuming due to the unstructured nature of natural language information. Deep learning based models have surpassed classical machine learning based approaches in various text classification tasks, including sentiment analysis, news categorisation, question answering, and natural language inference. In this paper, we provide a comprehensive review of most widespread deep learning based models for text classification developed in recent years, and discuss their technical contributions, similarities, and strengths.