Deep Sequence Models for Text Classification Tasks

TL;DR

This paper explores the application of deep sequence models like RNN, GRU, and LSTM to text classification tasks, demonstrating high accuracy and highlighting potential for further improvements in NLP applications.

Contribution

It applies advanced deep sequence models to text classification, showing their effectiveness and discussing avenues for future enhancement.

Findings

Models achieved 80-94% accuracy on classification tasks.

Sequence models effectively capture dependencies in text.

Room for improvement remains to match human performance.

Abstract

The exponential growth of data generated on the Internet in the current information age is a driving force for the digital economy. Extraction of information is the major value in an accumulated big data. Big data dependency on statistical analysis and hand-engineered rules machine learning algorithms are overwhelmed with vast complexities inherent in human languages. Natural Language Processing (NLP) is equipping machines to understand these human diverse and complicated languages. Text Classification is an NLP task which automatically identifies patterns based on predefined or undefined labeled sets. Common text classification application includes information retrieval, modeling news topic, theme extraction, sentiment analysis, and spam detection. In texts, some sequences of words depend on the previous or next word sequences to make full meaning; this is a challenging dependency task that requires the machine to be able to store some previous important information to impact future meaning. Sequence models such as RNN, GRU, and LSTM is a breakthrough for tasks with long-range dependencies. As such, we applied these models to Binary and Multi-class classification. Results generated were excellent with most of the models performing within the range of 80% and 94%. However, this result is not exhaustive as we believe there is room for improvement if machines are to compete with humans.