  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.
