Results 1 to 10 of about 218 (124)
Recognizing Textual Entailment: Challenges in the Portuguese Language † [PDF]
Information (Switzerland), 2018 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.
Gil Rocha +2 more
exaly +5 more sources
SNLI Indo: A recognizing textual entailment dataset in Indonesian derived from the Stanford Natural Language Inference dataset [PDF]
Data in BriefRecognizing textual entailment (RTE) is an essential task in natural language processing (NLP). It is the task of determining the inference relationship between text fragments (premise and hypothesis), of which the inference relationship is either ...
Daniel Siahaan, I Made Suwija Putra
exaly +4 more sources
Word2vec Based System for Recognizing Partial Textual Entailment [PDF]
Annals of computer science and information systems, 2016 Recognizing textual entailment is typically considered as a binary decision task - whether a text T entails a hypothesis H. Thus, in case of a negative answer, it is not possible to express that H is “almost entailed” by T. Partial textual entailment provides one possible approach to this issue.
Martin Víta, Vincent Kríž
doaj +3 more sources
Recognizing textual entailment: A review of resources, approaches, applications, and challenges
ICT ExpressThe review aims to examine the current state of recognizing textual entailment (RTE) research and summarize the state-of-the-art methods in the development of natural language processing (NLP) applications, the various approaches, datasets, and future ...
Daniel Siahaan, I Made Suwija Putra
exaly +3 more sources
Feature-Rich Classifiers for Recognizing Textual Entailment in Indonesian
Procedia Computer Science, 2021 Abstract Recognizing Textual Entailment (RTE) is a Natural Language Processing task to determine whether a sentence (text) semantically entails another sentence (hypothesis). In this paper, we extracted and learned 35 features from a pair of text and hypothesis in Indonesian.
Rahmad Mahendra
exaly +2 more sources
IEEE AccessRecognizing Textual Entailment (RTE) has become essential to determine inferential relationships between sentences in text-understanding systems. Traditionally, RTE models have addressed textual inferences at both syntactic and semantic levels.
Daniel Siahaan +2 more
exaly +3 more sources
Recognizing Textual Entailment with a Semantic Edit Distance Metric
2012 11th Mexican International Conference on Artificial Intelligence, 2012 We present a Recognizing Textual Entailment(RTE) system based on different similarity metrics. The metricsused are string-based metrics and the Semantic Edit DistanceMetric, which is proposed in this paper to address limitationsof known semantic-based metrics and to support the decisionsmade by a simple method based on lexical similarity metrics.We add
Alexander Gelbukh
exaly +3 more sources
Figurative Language in Recognizing Textual Entailment [PDF]
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, 2021 We introduce a collection of recognizing textual entailment (RTE) datasets focused on figurative language. We leverage five existing datasets annotated for a variety of figurative language -- simile, metaphor, and irony -- and frame them into over 12,500 RTE examples.We evaluate how well state-of-the-art models trained on popular RTE datasets capture ...
Tuhin Chakrabarty +3 more
openaire +3 more sources
Logic-Based Inference With Phrase Abduction Using Vision-and-Language Models
IEEE Access, 2023 Recognizing Textual Entailment (RTE) is among the most fundamental tasks in natural language processing applications, such as question answering and machine translation.
Akiyoshi Tomihari, Hitomi Yanaka
doaj +1 more source
A semantic approach to recognizing textual entailment [PDF]
Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing - HLT '05, 2005 Exhaustive extraction of semantic information from text is one of the formidable goals of state-of-the-art NLP systems. In this paper, we take a step closer to this objective. We combine the semantic information provided by different resources and extract new semantic knowledge to improve the performance of a recognizing textual entailment system.
Marta Tatu, Dan I. Moldovan
openaire +2 more sources