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Word2vec Based System for Recognizing Partial Textual Entailment

Martin Víta, Vincent Kríž
2016 Proceedings of the 2016 Federated Conference on Computer Science and Information Systems  
This paper presents an attempt to use word2vec model for recognizing partial (faceted) textual entailment.  ...  Recognizing textual entailment is typically considered as a binary decision task -whether a text T entails a hypothesis H.  ...  Main Aim of the Work In this paper we present a novel system for recognizing partial/faceted textual entailment that is based on word2vec representations of the words contained in the text T and words  ... 
doi:10.15439/2016f419 dblp:conf/fedcsis/VitaK16 fatcat:pbmp5szxzjcbdnwimkhcanmyhi

Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach [article]

Wenpeng Yin, Jamaal Hay, Dan Roth
2019 arXiv   pre-print
entailment formulation and study it this way.  ...  evaluation label-fully-unseen 0Shot-TC (Chang et al., 2008), aiming at classifying text snippets without seeing task specific training data at all. iii) We unify the 0Shot-TC of diverse aspects within a textual  ...  Acknowledgments The authors would like to thank Jennifer Sheffield and the anonymous reviewers for insightful comments and suggestions.  ... 
arXiv:1909.00161v1 fatcat:xybj7eseknholjlxdgoxyz7iru

Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach

Wenpeng Yin, Jamaal Hay, Dan Roth
2019 Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)  
., 2008) , aiming at classifying text snippets without seeing task specific training data at all. iii) We unify the 0SHOT-TC of diverse aspects within a textual entailment formulation and study it this  ...  Acknowledgments The authors would like to thank Jennifer Sheffield and the anonymous reviewers for insightful comments and suggestions.  ...  Approved for Public Release, Distribution Unlimited. The views expressed are those of the authors and do not reflect the official policy or position of the Department of Defense or the U.S.  ... 
doi:10.18653/v1/d19-1404 dblp:conf/emnlp/YinHR19 fatcat:65xsytlapfbdfagit4tpope4mm

Textual Inference with Tree-Structured LSTM [chapter]

Adebayo Kolawole John, Luigi Di Caro, Livio Robaldo, Guido Boella
2017 Communications in Computer and Information Science  
Textual Entailment (TE) is a specific task in Textual Inference that aims at determining whether a hypothesis is entailed by a text.  ...  This paper employs the Child-Sum Tree-LSTM for solving the challenging problem of textual entailment. Our approach is simple and able to generalize well without excessive parameter optimization.  ...  In particular, the authors in [26] using attention-based LSTM have reported state of the art results in textual entailment.  ... 
doi:10.1007/978-3-319-67468-1_2 fatcat:2uv2w4cdzzhgbhnrndaftltztu

Recognizing Textual Entailment: Challenges in the Portuguese Language

Gil Rocha, Henrique Lopes Cardoso
2018 Information  
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  ...  Recognizing textual entailment comprises the task of determining semantic entailment relations between text fragments.  ...  Henrique Lopes Cardoso has supervised the work, namely for defining the methods proposed, designing the experimental settings and analyzing results.  ... 
doi:10.3390/info9040076 fatcat:e7n6olpwvjbbjpo3wjss5wkoxy

Max-Cosine Matching Based Neural Models for Recognizing Textual Entailment [article]

Zhipeng Xie, Junfeng Hu
2017 arXiv   pre-print
Recognizing textual entailment is a fundamental task in a variety of text mining or natural language processing applications. This paper proposes a simple neural model for RTE problem.  ...  Besides the base model, in order to enhance its performance, we also proposed three techniques: the integration of multiple word-embedding library, bi-way integration, and ensemble based on model averaging  ...  We are grateful to the anonymous reviewers for their valuable comments.  ... 
arXiv:1705.09054v1 fatcat:x5mohptbwbc2jfpxvhvt6i3z7i

CogALex-V Shared Task: LexNET - Integrated Path-based and Distributional Method for the Identification of Semantic Relations [article]

Vered Shwartz, Ido Dagan
2016 arXiv   pre-print
for this task.  ...  for semantic relation classification.  ...  Acknowledgments This work was partially supported by an Intel ICRI-CI grant, the Israel Science Foundation grant 880/12, and the German Research Foundation through the German-Israeli Project Cooperation  ... 
arXiv:1610.08694v3 fatcat:7mrmqdvjardetmxk4yvsidwira

Evaluating semantic models with word-sentence relatedness [article]

Kimberly Glasgow, Matthew Roos, Amy Haufler, Mark Chevillet, Michael Wolmetz
2017 arXiv   pre-print
Semantic textual similarity (STS) systems are designed to encode and evaluate the semantic similarity between words, phrases, sentences, and documents.  ...  As a sample application of this relatedness data, behavior-based relatedness was compared to the relatedness computed via four off-the-shelf STS models: n-gram, Latent Semantic Analysis (LSA), Word2Vec  ...  Tim Finin, University of Maryland, Baltimore County, in making the code for the Ebiquity STS system available, Mary Luongo in helping to collect behavioral responses, and Dr.  ... 
arXiv:1603.07253v2 fatcat:2wrspxwbu5d5xngoammrvlclpe

Semantic Matching Against a Corpus: New Applications and Methods [article]

Lucy H. Lin, Scott Miles, Noah A. Smith
2018 arXiv   pre-print
This work provides a proof-of-concept for such applications of semantic matching and illustrates key challenges.  ...  On the latter, we find that a new model built from natural language entailment data produces higher-quality matches than simple word-vector averaging, both on expert-crafted queries and on ones produced  ...  and UW NLP for their comments on earlier drafts.  ... 
arXiv:1808.09502v1 fatcat:7wnuksqj6nfr3jir62du32awsy

ANU-CSIRO at MEDIQA 2019: Question Answering Using Deep Contextual Knowledge

Vincent Nguyen, Sarvnaz Karimi, Zhenchang Xing
2019 Proceedings of the 18th BioNLP Workshop and Shared Task  
We report on our system for textual inference and question entailment in the medical domain for the ACL BioNLP 2019 Shared Task, MEDIQA.  ...  Textual inference is the task of finding the semantic relationships between pairs of text. Question entailment involves identifying pairs of questions which have similar semantic content.  ...  ) define question entailment as the situation where "a question, Q 1 , entails another question, Q 2 , if every answer to Q 2 is also a complete or partial answer to Q 1 ."  ... 
doi:10.18653/v1/w19-5051 dblp:conf/bionlp/NguyenKX19 fatcat:kpl3nelghrbfzdwp3kzxqf6sxu

Discovering Implicit Knowledge with Unary Relations

Michael Glass, Alfio Gliozzo
2018 Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)  
This is a major problem for Knowledge Base Population, severely limiting recall.  ...  State-of-the-art relation extraction approaches are only able to recognize relationships between mentions of entity arguments stated explicitly in the text and typically localized to the same sentence.  ...  This is unsuitable for evaluating our approach because the system is able to make probabilistic predictions based on implicit and partial textual evidence, thus producing correct triples outside the classic  ... 
doi:10.18653/v1/p18-1147 dblp:conf/acl/GliozzoG18 fatcat:b7abczptazbvfmfvzkyqobliaq

RELLY: Inferring Hypernym Relationships Between Relational Phrases

Adam Grycner, Gerhard Weikum, Jay Pujara, James Foulds, Lise Getoor
2015 Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing  
., "is a relative of") are central for many tasks including question answering, open information extraction, paraphrasing, and entailment detection.  ...  Our graph induction approach integrates small highprecision knowledge bases together with large automatically curated resources, and reasons collectively to combine these resources into a consistent graph  ...  This concept was introduced in the Recognizing Textual Entailment (RTE) shared task (Dagan et al., 2005) .  ... 
doi:10.18653/v1/d15-1113 dblp:conf/emnlp/GrycnerWPFG15 fatcat:yn7qqbbsdngc5opgbwiv4bvd7e

ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs [article]

Wenpeng Yin, Hinrich Schütze, Bing Xiang, Bowen Zhou
2018 arXiv   pre-print
This work presents a general Attention Based Convolutional Neural Network (ABCNN) for modeling a pair of sentences. We make three contributions.  ...  How to model a pair of sentences is a critical issue in many NLP tasks such as answer selection (AS), paraphrase identification (PI) and textual entailment (TE).  ...  We would like to thank the anonymous reviewers for their helpful comments.  ... 
arXiv:1512.05193v4 fatcat:ngfucek2mng5npnq4o3hzawmam

ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs

Wenpeng Yin, Hinrich Schütze, Bing Xiang, Bowen Zhou
2016 Transactions of the Association for Computational Linguistics  
This work presents a general Attention Based Convolutional Neural Network (ABCNN) for modeling a pair of sentences. We make three contributions.  ...  How to model a pair of sentences is a critical issue in many NLP tasks such as answer selection (AS), paraphrase identification (PI) and textual entailment (TE).  ...  We would like to thank the anonymous reviewers for their helpful comments.  ... 
doi:10.1162/tacl_a_00097 fatcat:kn7p7lk4afdcpb25t2obssg75e

Ordinal Common-sense Inference [article]

Sheng Zhang, Rachel Rudinger, Kevin Duh, Benjamin Van Durme
2017 arXiv   pre-print
We propose an evaluation of automated common-sense inference based on an extension of recognizing textual entailment: predicting ordinal human responses on the subjective likelihood of an inference holding  ...  We describe a framework for extracting common-sense knowledge from corpora, which is then used to construct a dataset for this ordinal entailment task.  ...  Acknowledgments Thank you to action editor Mark Steedman and the anonymous reviewers for their feedback, as well as colleagues including Lenhart Schubert, Kyle Rawlins, Aaron White, and Keisuke Sakaguchi  ... 
arXiv:1611.00601v3 fatcat:kezt3fidsvgkxk5riypb3o7bfi
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