A Neuro Symbolic Approach for Contradiction Detection in Persian Text

Detection of semantic contradictory sentences is a challenging and fundamental issue for some NLP applications, such as textual entailments recognition. In this study, contradiction means different types of semantic confrontation, such as negation, antonymy, and numerical. Due to the lack of sufficient data to apply precise machine learning and, specifically, deep learning methods to Persian and other low-resource languages, rule-based approaches are of great interest. Also, recently, the emergence of new methods such as transfer learning has opened up the possibility of deep learning for low-resource languages. This paper introduces a hybrid contradiction detection approach for detecting seven categories of contradictions in Persian texts: Antonymy, negation, numerical, factive, structural, lexical and world knowledge. The proposed method consists of 1) a novel data mining method and 2) a transformer-based deep neural method for contradiction detection . Also, a simple baseline is presented for comparison. The data mining method uses frequent rule mining to extract appropriate contradiction detection rules employing a development set. Extracted rules are tested for different categories of contradictory sentences. In the first step, a classifier checks whether the rules work for an input sentence pair. Then, according to the result, rules are used for three categories of negation, numerical, and antonym. In this part, the highest F-measure is obtained for detecting the negation category (90%), the average F-measure for these three categories is 86%, and for the other four categories, in which the rules have a lower F-measure of 62%, the transformer-based method achieved 76%. The proposed hybrid approach has an overall f-measure of higher than 80%.&nbsp

Incorporation of models in automatic requirement dependencies detection

In this work, an approach to the automatic detection of dependencies in NL requirements is presented. The approach is based on an ontology containing knowledge defining dependency relations between requirements. In order to manage the requirements' information, NLP and ML algorithms are used

Contradiction Detection with Contradiction-Specific Word Embedding

Contradiction detection is a task to recognize contradiction relations between a pair of sentences. Despite the effectiveness of traditional context-based word embedding learning algorithms in many natural language processing tasks, such algorithms are not powerful enough for contradiction detection. Contrasting words such as “overfull” and “empty” are mostly mapped into close vectors in such embedding space. To solve this problem, we develop a tailored neural network to learn contradiction-specific word embedding (CWE). The method can separate antonyms in the opposite ends of a spectrum. CWE is learned from a training corpus which is automatically generated from the paraphrase database, and is naturally applied as features to carry out contradiction detection in SemEval 2014 benchmark dataset. Experimental results show that CWE outperforms traditional context-based word embedding in contradiction detection. The proposed model for contradiction detection performs comparably with the top-performing system in accuracy of three-category classification and enhances the accuracy from 75.97% to 82.08% in the contradiction category

Natural Language Processing: Emerging Neural Approaches and Applications

This Special Issue highlights the most recent research being carried out in the NLP field to discuss relative open issues, with a particular focus on both emerging approaches for language learning, understanding, production, and grounding interactively or autonomously from data in cognitive and neural systems, as well as on their potential or real applications in different domains