Textual Entailment for Arabic Language based on Lexical and Semantic Matching

Khader, Mariam; Awajan, Arafat; Alkouz, Akram

doi:10.21700/ijcis.2016.109

Cited by 9 publications

(3 citation statements)

References 7 publications

(9 reference statements)

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“…Another lexical-based approach is the system proposed in [19] that detects entailment by calculating word overlap and applying bigram matching between the premise and hypothesis. Additionally, the system in [7] detects NLI for Arabic by employing a semantic similarity measure and a word sense disambiguation process.…”

Section: Related Workmentioning

confidence: 99%

Investigating the Use of Different Recurrent Neural Networks for Natural Language Inference in Arabic

Ben-Sghaier¹,

Bakari²,

Néji³

2023

Preprint

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Natural language inference (NLI) is a subfield of natural language processing (NLP) that involves determining the logical relationship between two pieces of text, usually a premise and a hypothesis. The goal of an NLI system is to classify the inference relationship between the premise and the hypothesis into one of three categories, namely, entailment, contradiction and neutral. An understanding of this relationship is useful for performing several NLP tasks, such as, summarization, question-answering, information retrieval, etc. Given the potential of neural networks to handle complex natural language tasks, and the absence of prior research on their application to NLI in Arabic. In this context, we propose the investigation of different neural network models for the NLI task in Arabic language. Particularly, we carried out experiments using various types of recurrent neural networks, such as, Simple RNN, LSTM, GRU, Bi-LSTM, and BiGRU to find the one that performs best on the NLI task in Arabic. We performed our experiments using the existing datasets for Arabic NLI namely, ArbTE, XNLI, and ArNLI.

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Section: Related Workmentioning

confidence: 99%

Investigating the Use of Different Recurrent Neural Networks for Natural Language Inference in Arabic

Ben-Sghaier¹,

Bakari²,

Néji³

2023

Preprint

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“…In the Arabic language, only a small amount of research has been done in the RTE domain. Textual entailment in Arabic faces various challenges due to the features of the language [5,8,23]. One of these challenges is lexical ambiguity, which is the difficulty of processing texts with missing diacritics.…”

Section: Related Work On Arabic Languagementioning

confidence: 99%

“…Khader et al [23] applied a lexical analysis technique of textual entailment for the Arabic language. They added a semantic matching approach to enhance the precision of their system.…”

Section: Related Work On Arabic Languagementioning

confidence: 99%

Arnli: Arabic Natural Language Inference Entailment and Contradiction Detection

Jallad

Ghneim²

2023

csci

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Natural Language Inference (NLI) is a hot topic research in natural language processing, contradiction detection between sentences is a special case of NLI. This is considered a difficult NLP task which has a big influence when added as a component in many NLP applications, such as Question Answering Systems, text Summarization. Arabic Language is one of the most challenging low-resources languages in detecting contradictions due to its rich lexical, semantics ambiguity. We have created a dataset of more than 12k sentences and named ArNLI, that will be publicly available. Moreover, we have applied a new model inspired by Stanford contradiction detection proposed solutions on English language. We proposed an approach to detect contradictions between pairs of sentences in Arabic language using contradiction vector combined with language model vector as an input to machine learning model. We analyzed results of different traditional machine learning classifiers and compared their results on our created dataset (ArNLI) and on an automatic translation of both PHEME, SICK English datasets. Best results achieved using Random Forest classifier with an accuracy of 99%, 60%, 75% on PHEME, SICK and ArNLI respectively.

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Graph-based Arabic text semantic representation

Etaiwi

Awajan

2020

Information Processing & Management

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Textual Entailment for Arabic Language based on Lexical and Semantic Matching

Cited by 9 publications

References 7 publications

Investigating the Use of Different Recurrent Neural Networks for Natural Language Inference in Arabic

Investigating the Use of Different Recurrent Neural Networks for Natural Language Inference in Arabic

Arnli: Arabic Natural Language Inference Entailment and Contradiction Detection

Graph-based Arabic text semantic representation

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