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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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Quantitative analysis of treebanks using frequent subtree mining methods

Quantitative analysis of treebanks using frequent subtree mi...

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

作者： Martens, Scott Centrum Voor Computerlinguïstiek KU Leuven Blijde-Inkomststraat 13 3000 Leuven Belgium

ISBN: (纸本)193243254X

the first task of statistical computational linguistics, or any other type of datadriven processing of language, is the extraction of counts and distributions of phenomena. this is much more difficult for the type of complex structured data found in treebanks and in corpora with sophisticated annotation than for tokenized texts. Recent developments in data mining, particularly in the extraction of frequent subtrees from treebanks, offer some solutions. We have applied a modified version of the TreeMiner algorithm to a small treebank and present some promising results.

关键词： Extraction

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COLING 2022 - Proceedings of textgraphs-16: graph-based methods for natural language processing, NLP 2022 - 29th International Conference on Computational Linguistics

COLING 2022 - Proceedings of TextGraphs-16: Graph-based Meth...

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16th workshop on graph-based methods for natural language processing, textgraphs 2022, in conjunction with the 29th International Conference on Computational Linguistics, COLING 2022

the proceedings contain 15 papers. the topics discussed include: multilevel hypernode graphs for effective and efficient entity linking;cross-modal contextualized hidden state projection method for expanding of taxonomic graphs;sharing parameter by conjugation for knowledge graph embeddings in complex space;a clique-based graphical approach to detect interpretable adjectival senses in Hungarian;the effectiveness of masked language modeling and adapters for factual knowledge injection;text-aware graph embeddings for donation behavior prediction;word sense disambiguation of French lexicographical examples using lexical networks;temporal graph analysis of misinformation spreaders in social media;IJS at textgraphs-16 natural language premise selection task: will contextual information improve natural language premise selection?;and keyword-based natural language premise selection for an automatic mathematical statement proving.

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Knowgraph@IITK at SemEval-2021 Task 11: Building Knowledge graph for NLP Research 15

KnowGraph@IITK at SemEval-2021 Task 11: Building Knowledge G...

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15th International workshops on Semantic Evaluation (SemEval)

作者： Shailabh, Shashank Chaurasia, Sajal Modi, Ashutosh Indian Inst Technol Kanpur IIT Kanpur Kanpur Uttar Pradesh India

ISBN: (纸本)9781954085701

Research in natural language processing is making rapid advances, resulting in the publication of a large number of research papers. Finding relevant research papers and their contribution to the domain is a challenging problem. In this paper, we address this challenge via the SemEval 2021 Task 11: NLPContributiongraph, by developing a system for a research paper contributions-focused knowledge graph over natural language processing literature. the task is divided into three sub-tasks: extracting contribution sentences that show important contributions in the research article, extracting phrases from the contribution sentences, and predicting the information units in the research article together with triplet formation from the phrases. the proposed system is agnostic to the subject domain and can be applied for building a knowledge graph for any area. We found that transformer-based language models can significantly improve existing techniques and utilized the SciBERT-based model. Our first sub-task uses Bidirectional LSTM (BiLSTM) stacked on top of SciBERT model layers, while the second sub-task uses Conditional Random Field (CRF) on top of SciBERT with BiLSTM. the third sub-task uses a combined SciBERT based neural approach with heuristics for information unit prediction and triplet formation from the phrases. Our system achieved F1 score of 0.38, 0.63 and 0.76 in end-to-end pipeline testing, phrase extraction testing and triplet extraction testing respectively.

关键词： Knowledge graph

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ECNUICA at SemEval-2021 Task 11: Rule based Information Extraction Pipeline 15

ECNUICA at SemEval-2021 Task 11: Rule based Information Extr...

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15th International workshops on Semantic Evaluation (SemEval)

作者： Lin, Jiaju Ling, Jing Wang, Zhiwei Liu, Jiawei Chen, Qin He, Liang East China Normal Univ Sch Comp Sci & Technol Shanghai Peoples R China

ISBN: (纸本)9781954085701

this paper presents our endeavor for solving task11, NLPContributiongraph, of SemEval2021. the purpose of the task is to extract triples from a paper in the Nature language processing field for constructing an Open Research Knowledge graph. the task includes three sub-tasks: detecting the contribution sentences in papers, identifying scientific terms and predicate phrases from the contribution sentences;and inferring triples in the form of (subject, predicate, object) as statements for Knowledge graph building. In this paper, we apply an ensemble of various fine-tuned pretrained language models (PLM) for tasks one and two. In addition, the self-training methods are adopted for tackling the shortage of annotated data. For the third task, rather than using classic neural open information extraction (OIE) architectures, we generate potential triples via manually designed rules and develop a binary classifier to differentiate positive ones from others. the quantitative results show that we obtain the 4(th), 2(nd), and 2(nd) rank in three evaluation phases.

关键词： Knowledge graph

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textgraphs 2022 Shared Task on natural language Premise Selection 16

TextGraphs 2022 Shared Task on Natural Language Premise Sele...

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16th workshop on graph-based methods for natural language processing, textgraphs 2022, in conjunction with the 29th International Conference on Computational Linguistics, COLING 2022

作者： Valentino, Marco Ferreira, Deborah thayaparan, Mokanarangan Freitas, André Ustalov, Dmitry Idiap Research Institute Switzerland University of Manchester United Kingdom Toloka Serbia

the Shared Task on natural language Premise Selection (NLPS) asks participants to retrieve the set of premises that are most likely to be useful for proving a given mathematical statement from a supporting knowledge base. While previous editions of the textgraphs shared tasks series targeted multi-hop inference for explanation regeneration in the context of science questions (thayaparan et al., 2021;Jansen and Ustalov, 2020, 2019), NLPS aims to assess the ability of state-of-the-art approaches to operate on a mixture of natural and mathematical language and model complex multi-hop reasoning dependencies between statements. To this end, this edition of the shared task makes use of a large set of approximately 21k mathematical statements extracted from the PS-ProofWiki dataset (Ferreira and Freitas, 2020a). In this summary paper, we present the results of the 1st edition of the NLPS task, providing a description of the evaluation data, and the participating systems. Additionally, we perform a detailed analysis of the results, evaluating various aspects involved in mathematical language processing and multi-hop inference. the best-performing system achieved a MAP of 15.39, improving the performance of a TF-IDF baseline by approximately 3.0 MAP.1 © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Large dataset

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INNOVATORS at SemEval-2021 Task-11: A Dependency Parsing and BERT-based model for Extracting Contribution Knowledge from Scientific Papers 15

INNOVATORS at SemEval-2021 Task-11: A Dependency Parsing and...

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15th International workshops on Semantic Evaluation (SemEval)

作者： Arora, Hardik Ghosal, Tirthankar Kumar, Sandeep Patwal, Suraj Gooch, Phil Indian Inst Technol Patna Patna Bihar India Charles Univ Prague Inst Formal & Appl Linguist Prague Czech Republic

ISBN: (纸本)9781954085701

In this work, we describe our system submission to the SemEval 2021 Task 11: NLP Contribution graph Challenge. We attempt all the three sub-tasks in the challenge and report our results. Subtask 1 aims to identify the contributing sentences in a given publication. Subtask 2 follows from Subtask 1 to extract the scientific term and predicate phrases from the identified contributing sentences. the final Subtask 3 entails extracting triples (subject, predicate, object) from the phrases and categorizing them under one or more defined information units. With the NLPContributiongraph Shared Task, the organizers formalized the building of a scholarly contributions-focused graph over NLP scholarly articles as an automated task. Our approaches include a BERT-based classification model for identifying the contributing sentences in a research publication, a rule-based dependency parsing for phrase extraction, followed by a CNN-based model for information units classification and a set of rules for triples extraction. the quantitative results show that we obtain the 5th, 5th, and 7th rank respectively in three evaluation phases. We make our codes available at https://***/HardikArora17/ SemEval- 2021- INNOVATORS

关键词： natural language processing systems

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UIUC BioNLP at SemEval-2021 Task 11: A Cascade of Neural Models for Structuring Scholarly NLP Contributions 15

UIUC BioNLP at SemEval-2021 Task 11: A Cascade of Neural Mod...

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15th International workshops on Semantic Evaluation (SemEval)

作者： Liu, Haoyang Sarol, Janina Kilicoglu, Halil Univ Illinois Sch Informat Sci Urbana IL 61801 USA

ISBN: (纸本)9781954085701

We propose a cascade of neural models that performs sentence classification, phrase recognition, and triple extraction to automatically structure the scholarly contributions of NLP publications in English. To identify the most important contribution sentences in a paper, we used a BERT-based classifier with positional features (Subtask 1). A BERT-CRF model was used to recognize and characterize relevant phrases in contribution sentences (Subtask 2). We categorized the triples into several types based on whether and how their elements were expressed in text, and addressed each type using separate BERT-based classifiers as well as rules (Subtask 3). Our system was officially ranked second in Phase 1 evaluation and first in both parts of Phase 2 evaluation. After fixing a submission error in Phase 1, our approach yielded the best results overall. In this paper, in addition to a system description, we also provide further analysis of our results, highlighting its strengths and limitations. We make our code publicly available at https://***/ Liu-Hy/nlp-contrib- graph.

关键词： natural language processing systems

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Sharing Parameter by Conjugation for Knowledge graph Embeddings in Complex Space 16

Sharing Parameter by Conjugation for Knowledge Graph Embeddi...

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16th workshop on graph-based methods for natural language processing, textgraphs 2022, in conjunction with the 29th International Conference on Computational Linguistics, COLING 2022

作者： Feng, Xincan Qu, Zhi Cheng, Yuchang Watanabe, Taro Yugami, Nobuhiro Natural Language Processing Laboratory Nara Institute of Science and Technology Japan Multilingual Knowledge Computing Laboratory Fujitsu Ltd Japan

A Knowledge graph (KG) is the directed graphical representation of entities and relations in the real world. KG can be applied in diverse natural language processing (NLP) tasks where knowledge is required. the need to scale up and complete KG automatically yields Knowledge graph Embedding (KGE), a shallow machine learning model that is suffering from memory and training time consumption issues. To mitigate the computational load, we propose a parameter-sharing method, i.e., using conjugate parameters for complex numbers employed in KGE models. Our method improves memory efficiency by 2x in relation embedding while achieving comparable performance to the state-of-the-art non-conjugate models, with faster, or at least comparable, training time. We demonstrated the generalizability of our method on two best-performing KGE models 5*E (Nayyeri et al., 2021) and ComplEx (Trouillon et al., 2016) on five benchmark datasets. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Directed graphs

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Debunking sentiment lexicons: A case of domain-specific sentiment classification for Croatian 6

Debunking sentiment lexicons: A case of domain-specific sent...

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6th workshop on Balto-Slavic natural language processing, BSNLP 2017 at the 15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017

作者： Gombar, Paula Medić, Zoran Alagić, Domagoj Šnajder, Jan Text Analysis and Knowledge Engineering Lab Faculty of Electrical Engineering and Computing University of Zagreb Unska 3 Zagreb10000 Croatia

ISBN: (纸本)9781945626456

Sentiment lexicons are widely used as an intuitive and inexpensive way of tackling sentiment classification, often within a simple lexicon word-counting approach or as part of a supervised model. However, it is an open question whether these approaches can compete with supervised models that use only word-representation features. We address this question in the context of domain-specific sentiment classification for Croatian. We experiment with the graph-based acquisition of sentiment lexicons, analyze their quality, and investigate how effectively they can be used in sentiment classification. Our results indicate that, even with as few as 500 labeled instances, a supervised model substantially outperforms a word-counting model. We also observe that adding lexicon-based features does not significantly improve supervised sentiment classification. © 2017 Association for Computational Linguistics.

关键词： graphic methods

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Resolving duplicates in Large Multiple-Choice Questions Repositories 20

Resolving duplicates in Large Multiple-Choice Questions Repo...

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20th Conference on Information and Research Science Connecting to Digital and Library Science, IRCDL 2024

作者： Albano, Valentina Firmani, Donatella Laura, Luigi Mathew, Jerin George Paoletti, Anna Lucia Torrente, Irene Dip. Funzione Pubblica Corso Vittorio Emanuele II 116 Rome00186 Italy Sapienza University Piazzale Aldo Moro 5 Rome00185 Italy Uninettuno University Corso Vittorio Emanuele II 39 Rome00186 Italy Formez Viale Marx 15 Rome00137 Italy

Multiple-choice questions (MCQs) are commonly used in educational assessments and professional certification examinations. However, managing vast collections of MCQs presents numerous challenges, including maintaining their quality and relevance. A notable issue in such repositories is the occurrence of conceptually identical questions presented in varied forms. these duplicates, while different in wording, fail to enhance the value of the repository. In this extended abstract, we present our approach for identifying and handling potential duplicate questions in large MCQ databases. Our proposed method involves three primary stages: initial pre-processing of MCQs, calculation of similarity based on natural language processing (NLP) techniques, and a graph-based method for exploring these similarities. © 2021 Copyright for this paper by its authors.

关键词： graphic methods

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