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Lexicosyntactic Inference in Neural Models [article]

Aaron Steven White, Rachel Rudinger, Kyle Rawlins, Benjamin Van Durme
2018 arXiv   pre-print
We investigate neural models' ability to capture lexicosyntactic inferences: inferences triggered by the interaction of lexical and syntactic information.  ...  We take the task of event factuality prediction as a case study and build a factuality judgment dataset for all English clause-embedding verbs in various syntactic contexts.  ...  The views and conclusions contained in this publication are those of the authors and should not be interpreted as representing official policies or endorsements of DARPA or the U.S. Government.  ... 
arXiv:1808.06232v1 fatcat:hhuftyvnjna55pv5uyipec6b2q

Lexicosyntactic Inference in Neural Models

Aaron Steven White, Rachel Rudinger, Kyle Rawlins, Benjamin Van Durme
2018 Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing  
We investigate neural models' ability to capture lexicosyntactic inferences: inferences triggered by the interaction of lexical and syntactic information.  ...  We take the task of event factuality prediction as a case study and build a factuality judgment dataset for all English clause-embedding verbs in various syntactic contexts.  ...  The views and conclusions contained in this publication are those of the authors and should not be interpreted as representing official policies or endorsements of DARPA or the U.S. Government.  ... 
doi:10.18653/v1/d18-1501 dblp:conf/emnlp/WhiteRRD18 fatcat:kndiqv744bhtjhu6pgn65poejy

Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation

Adam Poliak, Aparajita Haldar, Rachel Rudinger, J. Edward Hu, Ellie Pavlick, Aaron Steven White, Benjamin Van Durme
2018 Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing  
The collection results from recasting 13 existing datasets from 7 semantic phenomena into a common NLI structure, resulting in over half a million labeled context-hypothesis pairs in total.  ...  We refer to our collection as the DNC: Diverse Natural Language Inference Collection.  ...  The views and conclusions contained in this publication are those of the authors and should not be interpreted as representing official policies or endorsements of DARPA or the U.S. Government.  ... 
doi:10.18653/v1/d18-1007 dblp:conf/emnlp/PoliakHRHPWD18 fatcat:baoa6zqnzzfklmexao77rmqkfe

Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation [article]

Adam Poliak, Aparajita Haldar, Rachel Rudinger, J. Edward Hu, Ellie Pavlick, Aaron Steven White, Benjamin Van Durme
2018 arXiv   pre-print
The collection results from recasting 13 existing datasets from 7 semantic phenomena into a common NLI structure, resulting in over half a million labeled context-hypothesis pairs in total.  ...  We refer to our collection as the DNC: Diverse Natural Language Inference Collection.  ...  The views and conclusions contained in this publication are those of the authors and should not be interpreted as representing official policies or endorsements of DARPA or the U.S. Government.  ... 
arXiv:1804.08207v2 fatcat:5mnayjchjzberc6g2o6nbmb7wy

Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation

Adam Poliak, Aparajita Haldar, Rachel Rudinger, J. Edward Hu, Ellie Pavlick, Aaron Steven White, Benjamin Van Durme
2018 Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP  
We present a large scale collection of diverse natural language inference (NLI) datasets that help provide insight into how well a sentence representation encoded by a neural network captures distinct  ...  The collection results from recasting 13 existing datasets from 7 semantic phenomena into a common NLI structure, resulting in over half a million labeled context-hypothesis pairs in total.  ...  The tasks include event factuality, named entity recognition, gendered anaphora resolution, sentiment analysis, relationship extraction, pun detection, and lexicosyntactic inference (Table 2) .  ... 
doi:10.18653/v1/w18-5441 dblp:conf/emnlp/PoliakHRHPWD18a fatcat:jgh6i4foxrdajbzjndcojer7mi

ON USAGE OF MACHINE LEARNING FOR NATURAL LANGUAGE PROCESSING TASKS AS ILLUSTRATED BY EDUCATIONAL CONTENT MINING
ОБ ИСПОЛЬЗОВАНИИ МАШИННОГО ОБУЧЕНИЯ В ЗАДАЧАХ ОБРАБОТКИ ЕСТЕСТВЕННОГО ЯЗЫКА НА ПРИМЕРЕ АНАЛИЗА ОБРАЗОВАТЕЛЬНОГО КОНТЕНТА

A.V. Melnikov, D.S. Botov, J.D. Klenin
2017 Ontology of Designing  
in conjunction with deep syntactic and semantic analysis using various deep neural networks.  ...  synthesizing educational content in a form of a decision support systems.  ...  Language Models Lexicosyntactic rules Knowledgebased models The most simplistic approach to language modeling includes various statistical models, based primarily on word distributions within the document  ... 
doi:10.18287/2223-9537-2017-7-1-34-47 fatcat:46a7pqjlbjbelklnnam424fp24

Improving Hypernymy Detection with an Integrated Path-based and Distributional Method [article]

Vered Shwartz, Yoav Goldberg, Ido Dagan
2016 arXiv   pre-print
We suggest an improved path-based algorithm, in which the dependency paths are encoded using a recurrent neural network, that achieves results comparable to distributional methods.  ...  Detecting hypernymy relations is a key task in NLP, which is addressed in the literature using two complementary approaches.  ...  Acknowledgments We would like to thank Omer Levy for his involvement and assistance in the early stage of this project and Enrico Santus for helping us by computing the results of SLQS (Santus et al.,  ... 
arXiv:1603.06076v3 fatcat:zsn34iw3unckzkwqwys4lf6zue

Improving Hypernymy Detection with an Integrated Path-based and Distributional Method

Vered Shwartz, Yoav Goldberg, Ido Dagan
2016 Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)  
We suggest an improved path-based algorithm, in which the dependency paths are encoded using a recurrent neural network, that achieves results comparable to distributional methods.  ...  Detecting hypernymy relations is a key task in NLP, which is addressed in the literature using two complementary approaches.  ...  Acknowledgments We would like to thank Omer Levy for his involvement and assistance in the early stage of this project and Enrico Santus for helping us by computing the results of SLQS (Santus et al.,  ... 
doi:10.18653/v1/p16-1226 dblp:conf/acl/ShwartzGD16 fatcat:yljga7p5t5borfg5bneh4s25bi

A Survey on Recognizing Textual Entailment as an NLP Evaluation [article]

Adam Poliak
2020 arXiv   pre-print
In this survey paper, we provide an overview of different approaches for evaluating and understanding the reasoning capabilities of NLP systems.  ...  We then focus our discussion on RTE by highlighting prominent RTE datasets as well as advances in RTE dataset that focus on specific linguistic phenomena that can be used to evaluate NLP systems on a fine-grained  ...  inference capabilities of NLP models.  ... 
arXiv:2010.03061v1 fatcat:jfmgkh4ginalzauawlqdbkb6pq

Sentence-Level Subjectivity Detection Using Neuro-Fuzzy Models

Samir Rustamov, Elshan Mustafayev, Mark Clements
2013 Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis  
In this work, we attempt to detect sentencelevel subjectivity by means of two supervised machine learning approaches: a Fuzzy Control System and Adaptive Neuro-Fuzzy Inference System.  ...  For this reason, these machine learning models can be applied to any language; i.e., there is no lexical, grammatical, syntactical analysis used in the classification process.  ...  Correct Subjectivity detection using Adaptive Neuro Fuzzy Inference System Fig. 1 illustrates the general structure of Adaptive Neuro Fuzzy Inference System.  ... 
dblp:conf/wassa/RustamovMC13 fatcat:k3b6gmjfgjftbhdueeoxuizg5q

Adverse drug events and medication relation extraction in electronic health records with ensemble deep learning methods

2019 JAMIA Journal of the American Medical Informatics Association  
For the inter-sentence relations, we adopted a neural architecture that utilizes the Transformer network to improve performance in longer sequences.  ...  The intra-sentence models rely on bidirectional long short-term memory networks and attention mechanisms and are able to capture dependencies between multiple related pairs in the same sentence.  ...  to thank the Institute of Advanced Industrial Science and Technology/Artificial Intelligence Research Center for providing the computational resources for the realization of the experiments conducted in  ... 
doi:10.1093/jamia/ocz101 pmid:31390003 pmcid:PMC6913215 fatcat:l2ciicjt6zaqniafn7ibkfef3e

Open Relation Extraction in Patent Claims with a Hybrid Network

Boting Geng, Wenqing Wu
2021 Wireless Communications and Mobile Computing  
the-state-of-art neural network classification models in the measures of Precision, Recall, and F1.  ...  In this paper, we propose an Open Relation Extraction (ORE) approach with transforming relation extraction problem into sequence labeling problem in patent claims, which extract none predefined relationship  ...  From a series of experiments in Table 4 above, we obviously conclude that hybrid neural network model performs better than traditional neural network model like Bi-LSTM, such as model 1and model 2 in  ... 
doi:10.1155/2021/5547281 fatcat:mduxto5zzzacfanawwrbg4acay

Learning Antonyms with Paraphrases and a Morphology-Aware Neural Network

Sneha Rajana, Chris Callison-Burch, Marianna Apidianaki, Vered Shwartz
2017 Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017)  
We further propose a neural network model, AntNET, that integrates morphological features indicative of antonymy into a path-based relation detection algorithm.  ...  We demonstrate that our model outperforms state-of-the-art models in distinguishing antonyms from other semantic relations and is capable of efficiently handling multi-word expressions.  ...  Acknowledgments This material is based in part on research sponsored by DARPA under grant number FA8750-13-2-0017 (the DEFT program). The U.S.  ... 
doi:10.18653/v1/s17-1002 dblp:conf/starsem/RajanaCAS17 fatcat:2lnueipczzbazjbztdjx3whgvi

Learning Taxonomies of Concepts and not Words using Contextualized Word Representations: A Position Paper [article]

Lukas Schmelzeisen, Steffen Staab
2019 arXiv   pre-print
In 2018, an emerging trend in NLP have been task-independent deep neural network architectures based on language model pre-training, which have achieved state-of-the-art results in a number of competitive  ...  Pattern-based approaches rely on word pairs occurring in specific lexicosyntactic patterns in a corpus (Roller, Kiela, and Nickel 2018).  ... 
arXiv:1902.02169v1 fatcat:xehjdyjdnfb23fxiyf7wlyw4wq

LHD 2.0: A Text Mining Approach to Typing Entities in Knowledge Graphs

Tomas Kliegr, Onddej Zamazal
2016 Social Science Research Network  
For lexicosyntactic (Hearst) pattern-based extraction we use our previously published Linked Hypernyms Dataset Framework.  ...  In this article, we introduce a novel technique for type inference that extracts types from the free text description of the entity combining lexico-syntactic pattern analysis with supervised classification  ...  Ondřej Zamazal has been additionally supported by the CSF grant no. 14-14076P, "COSOL -Categorization of Ontologies in Support of Ontology Life Cycle".  ... 
doi:10.2139/ssrn.3199238 fatcat:mmw5m2b55veknc5kaspfmpam5m
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