Feature based Entailment Recognition for Malayalam Language Texts

Abstract

Textual entailment is a relationship between two text fragments, namely, text/premise and hypothesis. It has applications in question answering systems, multi-document summarization, information retrieval systems, and social network analysis. In the era of the digital world, recognizing semantic variability is important in understanding inferences in texts. The texts are either in the form of sentences, posts, tweets, or user experiences. Hence understanding inferences from customer experiences helps companies in customer segmentation. The availability of digital information is ever-growing with textual data in almost all languages, including low resource languages. This work deals with various machine learning approaches applied to textual entailment recognition or natural language inference for Malayalam, a South Indian low resource language. A performance-based analysis using machine learning classification techniques such as Logistic Regression, Decision Tree, Support Vector Machine, Random Forest, AdaBoost, and Naive Bayes is done for the MaNLI (Malayalam Natural Language Inference) dataset. Different lexical and surface-level features are used for this binary and multiclass classification. With the increasing size of the dataset, there is a drop in the performance of feature-based classification. A comparison of feature-based models with deep learning approaches highlights this inference. The main focus here is the feature-based analysis with 14 different features and its comparison, essential to any NLP classification problem.