A Study of the Effect of Resolving Negation and Sentiment Analysis in Recognizing Text Entailment for Arabic
It addresses a specific issue in Arabic natural language processing, focusing on a language with limited prior work, but the approach is incremental as it builds on existing methods by adding negation and sentiment features.
This paper tackles the problem of improving Arabic text entailment accuracy by resolving negation and analyzing sentiment polarity in text-hypothesis pairs, resulting in an increased entailment accuracy as demonstrated on a dataset of 618 pairs.
Recognizing the entailment relation showed that its influence to extract the semantic inferences in wide-ranging natural language processing domains (text summarization, question answering, etc.) and enhanced the results of their output. For Arabic language, few attempts concerns with Arabic entailment problem. This paper aims to increase the entailment accuracy for Arabic texts by resolving negation of the text-hypothesis pair and determining the polarity of the text-hypothesis pair whether it is Positive, Negative or Neutral. It is noticed that the absence of negation detection feature gives inaccurate results when detecting the entailment relation since the negation revers the truth. The negation words are considered stop words and removed from the text-hypothesis pair which may lead wrong entailment decision. Another case not solved previously, it is impossible that the positive text entails negative text and vice versa. In this paper, in order to classify the text-hypothesis pair polarity, a sentiment analysis tool is used. We show that analyzing the polarity of the text-hypothesis pair increases the entailment accuracy. to evaluate our approach we used a dataset for Arabic textual entailment (ArbTEDS) consisted of 618 text-hypothesis pairs and showed that the Arabic entailment accuracy is increased by resolving negation for entailment relation and analyzing the polarity of the text-hypothesis pair.