Textual Entailment Recognition with Semantic Features from Empirical Text Representation

TL;DR

This paper introduces a novel semantic feature based on empirical thresholding and Manhattan distance for textual entailment recognition, outperforming classical methods in understanding sentence meaning.

Contribution

It proposes a new semantic feature using threshold-based text representation and Manhattan distance, improving entailment classification accuracy over traditional word embedding approaches.

Findings

Empirical semantic features outperform classical features.

The approach achieves higher accuracy on the SICK-RTE dataset.

Semantic enrichment enhances textual entailment recognition.

Abstract

Textual entailment recognition is one of the basic natural language understanding(NLU) tasks. Understanding the meaning of sentences is a prerequisite before applying any natural language processing(NLP) techniques to automatically recognize the textual entailment. A text entails a hypothesis if and only if the true value of the hypothesis follows the text. Classical approaches generally utilize the feature value of each word from word embedding to represent the sentences. In this paper, we propose a novel approach to identifying the textual entailment relationship between text and hypothesis, thereby introducing a new semantic feature focusing on empirical threshold-based semantic text representation. We employ an element-wise Manhattan distance vector-based feature that can identify the semantic entailment relationship between the text-hypothesis pair. We carried out several experiments on a benchmark entailment classification(SICK-RTE) dataset. We train several machine learning(ML) algorithms applying both semantic and lexical features to classify the text-hypothesis pair as entailment, neutral, or contradiction. Our empirical sentence representation technique enriches the semantic information of the texts and hypotheses found to be more efficient than the classical ones. In the end, our approach significantly outperforms known methods in understanding the meaning of the sentences for the textual entailment classification task.