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检索条件"任意字段=15th Workshop on Graph-Based Methods for Natural Language Processing, TextGraphs 2021"

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textgraphs 2021 - graph-based methods for natural language processing, Proceedings of the 15th workshop - in conjunction with the 2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics, NAACL 2021

TextGraphs 2021 - Graph-Based Methods for Natural Language P...

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15th workshop on graph-based methods for natural language processing, textgraphs 2021

ISBN: (纸本)9781954085381

the proceedings contain 20 papers. the topics discussed include: bootstrapping large-scale fine-grained contextual advertising classifier from Wikipedia;modeling graph structure via relative position for text generation from knowledge graphs;entity prediction in knowledge graphs with joint embeddings;hierarchical graph convolutional networks for jointly resolving cross-document coreference of entity and event mentions;learning clause representation from dependency-anchor graph for connective prediction;selective attention based graph convolutional networks for aspect-level sentiment classification;keyword extraction using unsupervised learning on the document’s adjacency matrix;improving human text simplification with sentence fusion;and on geodesic distances and contextual embedding compression for text classification.

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Proceedings of textgraphs@NAACL-HLT 2016: the 10th workshop on graph-based methods for natural language processing

Proceedings of TextGraphs@NAACL-HLT 2016: The 10th Workshop ...

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10th workshop on graph-based methods for natural language processing, textgraphs 2016, in conjunction with the 15th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human language Technologies, NAACL-HLT 2016

ISBN: (纸本)9781941643884

the proceedings contain 6 papers. the topics discussed include: embedding senses for efficient graph-based word sense disambiguation;context tailoring for text normalization;cross-lingual question answering using common semantic space;network motifs may improve quality assessment of text documents;better together: combining language and social interactions into a shared representation;and visualization of dynamic reference graphs.

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textgraphs-15 Shared Task System Description : Multi-Hop Inference Explanation Regeneration by Matching Expert Ratings 15

Textgraphs-15 Shared Task System Description : Multi-Hop Inf...

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15th workshop on graph-based methods for natural language processing, textgraphs 2021

作者： Kalyan, Vivek Witteveen, Sam Andrews, Martin Singapore Singapore Red Dragon AI Singapore

ISBN: (纸本)9781954085381

Creating explanations for answers to science questions is a challenging task that requires multi-hop inference over a large set of fact sentences. this year, to refocus the textgraphs Shared Task on the problem of gathering relevant statements (rather than solely finding a single 'correct path'), the WorldTree dataset was augmented with expert ratings of 'relevance' of statements to each overall explanation. Our system, which achieved second place on the Shared Task leaderboard, combines initial statement retrieval;language models trained to predict the relevance scores;and ensembling of a number of the resulting rankings. Our code implementation is made available at https: //***/mdda/worldtree_ corpus/tree/textgraphs_2021. © 2021 Association for Computational Linguistics.

关键词： Reforestation

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MG-BERT: Multi-graph Augmented BERT for Masked language Modeling 15

MG-BERT: Multi-Graph Augmented BERT for Masked Language Mode...

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15th workshop on graph-based methods for natural language processing, textgraphs 2021

作者： Ghader, Parishad Behnam Zakerinia, Hossein Baghshah, Mahdieh Soleymani Sharif University of Technology Tehran Iran

ISBN: (纸本)9781954085381

Pre-trained models like Bidirectional Encoder Representations from Transformers (BERT), have recently made a big leap forward in natural language processing (NLP) tasks. However, there are still some shortcomings in the Masked language Modeling (MLM) task performed by these models. In this paper, we first introduce a multi-graph including different types of relations between words. then, we propose Multi-graph augmented BERT (MG-BERT) model that is based on BERT. MG-BERT embeds tokens while taking advantage of a static multi-graph containing global word co-occurrences in the text corpus beside global real-world facts about words in knowledge graphs. the proposed model also employs a dynamic sentence graph to capture local context effectively. Experimental results demonstrate that our model can considerably enhance the performance in the MLM task. © 2021 Association for Computational Linguistics.

关键词： Knowledge graph

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Improving Human Text Simplification with Sentence Fusion 15

Improving Human Text Simplification with Sentence Fusion

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15th workshop on graph-based methods for natural language processing, textgraphs 2021

作者： Schwarzer, Max Tanprasert, Teerapaun Kauchak, David Mila University of Montreal Canada Computer Science Department Pomona College United States

ISBN: (纸本)9781954085381

the quality of fully automated text simplification systems is not good enough for use in real-world settings;instead, human simplifications are used. In this paper, we examine how to improve the cost and quality of human simplifications by leveraging crowdsourcing. We introduce a graph-based sentence fusion approach to augment human simplifications and a reranking approach to both select high quality simplifications and to allow for targeting simplifications with varying levels of simplicity. Using the Newsela dataset (Xu et al., 2015) we show consistent improvements over experts at varying simplification levels and find that the additional sentence fusion simplifications allow for simpler output than the human simplifications alone. © 2021 Association for Computational Linguistics.

关键词： graphic methods

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DeepBlueAI at textgraphs 2021 Shared Task: Treating Multi-Hop Inference Explanation Regeneration as A Ranking Problem 15

DeepBlueAI at TextGraphs 2021 Shared Task: Treating Multi-Ho...

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15th workshop on graph-based methods for natural language processing, textgraphs 2021

作者： Pan, Chunguang Song, Bingyan Luo, Zhipeng Co. Ltd China

ISBN: (纸本)9781954085381

this paper describes the winning system for textgraphs 2021 shared task: Multi-hop inference explanation regeneration. Given a question and its corresponding correct answer, this task aims to select the facts that can explain why the answer is correct for that question and answering (QA) from a large knowledge base. To address this problem and accelerate training as well, our strategy includes two steps. First, fine-tuning pre-trained language models (PLMs) with triplet loss to recall top-K relevant facts for each question and answer pair. then, adopting the same architecture to train the re-ranking model to rank the top-K candidates. To further improve the performance, we average the results from models based on different PLMs (e.g., RoBERTa) and different parameter settings to make the final predictions. the official evaluation shows that, our system can outperform the second best system by 4.93 points, which proves the effectiveness of our system. Our code has been open source, address is https://***/DeepBlueAI/textgraphs-15. © 2021 Association for Computational Linguistics.

关键词： Knowledge based systems

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Embedding Senses for Efficient graph-based Word Sense Disambiguation 10

Embedding Senses for Efficient Graph-based Word Sense Disamb...

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作者： Piña, Luis Nieto Johansson, Richard University of Gothenburg Sweden

ISBN: (纸本)9781941643884

We propose a simple graph-based method for word sense disambiguation (WSD) where sense and context embeddings are constructed by applying the Skip-gram method to random walks over the sense graph. We used this method to build a WSD system for Swedish using the SALDO lexicon, and evaluated it on six different annotated test sets. In all cases, our system was several orders of magnitude faster than a state-of-the-art PageRank-based system, while outperforming a random baseline soundly. © 2016 Association for Computational Linguistics

关键词： graphic methods

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Better together: Combining language and social interactions into a shared representation 10

Better together: Combining language and social interactions ...

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作者： Lai, Yi-Yu Li, Chang Goldwasser, Dan Neville, Jennifer Department of Computer Science Purdue University West LafayetteIN United States

ISBN: (纸本)9781941643884

Despite the clear inter-dependency between analyzing the interactions in social networks, and analyzing the natural language content of these interactions, these aspects are typically studied independently. In this paper we present a first step towards finding a joint representation, by embedding the two aspects into a single vector space. We show that the new representation can help improve performance in two social relations prediction tasks. © 2016 Association for Computational Linguistics

关键词： Vector spaces

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Cohesion graph based approach for unsupervised recognition of literal and non-literal use of multiword expressions

Cohesion graph based approach for unsupervised recognition o...

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4th workshop on graph-based methods for natural language processing, textgraphs 2009

作者： Li, Linlin Sporleder, Caroline Saarland University Postfach 15 11 50 66041 Saarbrücken Germany Germany

ISBN: (纸本)193243254X

We present a graph-based model for representing the lexical cohesion of a discourse. In the graph structure, vertices correspond to the content words of a text and edges connecting pairs of words encode how closely the words are related semantically. We show that such a structure can be used to distinguish literal and non-literal usages of multi-word expressions.

关键词： graphic methods

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textgraphs 2021 Shared Task on Multi-Hop Inference for Explanation Regeneration 15

TextGraphs 2021 Shared Task on Multi-Hop Inference for Expla...

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15th workshop on graph-based methods for natural language processing, textgraphs 2021

作者： thayaparan, Mokanarangan Valentino, Marco Jansen, Peter Ustalov, Dmitry Department of Computer Science University of Manchester United Kingdom School of Information University of Arizona United States Crowdsourcing Research Group Yandex Russia

ISBN: (纸本)9781954085381

the Shared Task on Multi-Hop Inference for Explanation Regeneration asks participants to compose large multi-hop explanations to questions by assembling large chains of facts from a supporting knowledge base. While previous editions of this shared task aimed to evaluate explanatory completeness - finding a set of facts that form a complete inference chain, without gaps, to arrive from question to correct answer, this 2021 instantiation concentrates on the subtask of determining relevance in large multi-hop explanations. To this end, this edition of the shared task makes use of a large set of approximately 250k manual explanatory relevancy ratings that augment the 2020 shared task data. In this summary paper, we describe the details of the explanation regeneration task, the evaluation data, and the participating systems. Additionally, we perform a detailed analysis of participating systems, evaluating various aspects involved in the multi-hop inference process. the best performing system achieved an NDCG of 0.82 on this challenging task, substantially increasing performance over baseline methods by 32%, while also leaving significant room for future improvement. © 2021 Association for Computational Linguistics.

关键词： Knowledge based systems

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