# A Study of the Effect of Resolving Negation and Sentiment Analysis in Recognizing Text Entailment for Arabic

arXiv: https://arxiv.org/abs/1907.03871 · HTML: https://mlsanity.com/paper/1907.03871/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jul 2019.

## Built on by (most cited first, 2 of 2)

- [ArNLI: Arabic Natural Language Inference for Entailment and Contradiction Detection](https://mlsanity.com/paper/2209.13953/) (2022)
- [Toward Zero-Shot Instruction Following](https://mlsanity.com/paper/2308.03795/) (2023)

## Abstract (arXiv)

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.