Paper Info
Title
Recognizing Textual Entailment: Challenges In The Portuguese Language
Abstract
Recognizing textual entailment comprises the task of determining semantic entailment relations between text fragments. A text fragment entails another text fragment if, from the meaning of the former, one can infer the meaning of the latter. If such relation is bidirectional, then we are in the presence of a paraphrase. Automatically recognizing textual entailment relations captures major semantic inference needs in several natural language processing (NLP) applications. As in many NLP tasks, textual entailment corpora for English abound, while the same is not true for more resource-scarce languages such as Portuguese. Exploiting what seems to be the only Portuguese corpus for textual entailment and paraphrases (the ASSIN corpus), in this paper, we address the task of automatically recognizing textual entailment (RTE) and paraphrases from text written in the Portuguese language, by employing supervised machine learning techniques. We employ lexical, syntactic and semantic features, and analyze the impact of using semantic-based approaches in the performance of the system. We then try to take advantage of the bi-dialect nature of ASSIN to compensate its limited size. With the same aim, we explore modeling the task of recognizing textual entailment and paraphrases as a binary classification problem by considering the bidirectional nature of paraphrases as entailment relationships. Addressing the task as a multi-class classification problem, we achieve results in line with the winner of the ASSIN Challenge. In addition, we conclude that semantic-based approaches are promising in this task, and that combining data from European and Brazilian Portuguese is less straightforward than it may initially seem. The binary classification modeling of the problem does not seem to bring advantages to the original multi-class model, despite the outstanding results obtained by the binary classifier for recognizing textual entailments.
Year
DOI
Venue
2018
10.3390/info9040076
INFORMATION
Keywords
Field
DocType
artificial intelligence, machine learning, natural language processing, recognizing textual entailment, paraphrase detection
Logical consequence,Textual entailment,Binary classification,Computer science,Inference,Portuguese,Paraphrase,Artificial intelligence,Natural language processing,Syntax,Brazilian Portuguese,Machine learning
Journal
Volume
Issue
ISSN
9
4
2078-2489
Citations 
PageRank 
References 
2
0.39
4
Authors
2
Name
Order
Citations
PageRank
Gil Rocha122.75
Henrique Lopes Cardoso222334.02