Feature based Entailment Recognition for Malayalam Language Texts

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