{"title":"基于 BERT 的序列深度神经架构,识别科学出版物中的贡献声明并提取三联短语","authors":"Komal Gupta, Ammaar Ahmad, Tirthankar Ghosal, Asif Ekbal","doi":"10.1007/s00799-023-00393-y","DOIUrl":null,"url":null,"abstract":"<p>Research in Natural Language Processing (NLP) is increasing rapidly; as a result, a large number of research papers are being published. It is challenging to find the contributions of the research paper in any specific domain from the huge amount of unstructured data. There is a need for structuring the relevant contributions in Knowledge Graph (KG). In this paper, we describe our work to accomplish four tasks toward building the Scientific Knowledge Graph (SKG). We propose a pipelined system that performs contribution sentence identification, phrase extraction from contribution sentences, Information Units (IUs) classification, and organize phrases into triplets (<i>subject, predicate, object</i>) from the NLP scholarly publications. We develop a multitasking system (ContriSci) for contribution sentence identification with two supporting tasks, <i>viz.</i> <i>Section Identification</i> and <i>Citance Classification</i>. We use the Bidirectional Encoder Representations from Transformers (BERT)—Conditional Random Field (CRF) model for the phrase extraction and train with two additional datasets: <i>SciERC</i> and <i>SciClaim</i>. To classify the contribution sentences into IUs, we use a BERT-based model. For the triplet extraction, we categorize the triplets into five categories and classify the triplets with the BERT-based classifier. Our proposed approach yields the F1 score values of 64.21%, 77.47%, 84.52%, and 62.71% for the contribution sentence identification, phrase extraction, IUs classification, and triplet extraction, respectively, for non-end-to-end setting. The relative improvement for contribution sentence identification, IUs classification, and triplet extraction is 8.08, 2.46, and 2.31 in terms of F1 score for the <i>NLPContributionGraph</i> (NCG) dataset. Our system achieves the best performance (57.54% F1 score) in the end-to-end pipeline with all four sub-tasks combined. 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There is a need for structuring the relevant contributions in Knowledge Graph (KG). In this paper, we describe our work to accomplish four tasks toward building the Scientific Knowledge Graph (SKG). We propose a pipelined system that performs contribution sentence identification, phrase extraction from contribution sentences, Information Units (IUs) classification, and organize phrases into triplets (<i>subject, predicate, object</i>) from the NLP scholarly publications. We develop a multitasking system (ContriSci) for contribution sentence identification with two supporting tasks, <i>viz.</i> <i>Section Identification</i> and <i>Citance Classification</i>. We use the Bidirectional Encoder Representations from Transformers (BERT)—Conditional Random Field (CRF) model for the phrase extraction and train with two additional datasets: <i>SciERC</i> and <i>SciClaim</i>. To classify the contribution sentences into IUs, we use a BERT-based model. 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引用次数: 0
摘要
自然语言处理(NLP)领域的研究正在迅速发展,因此也有大量研究论文发表。要从海量的非结构化数据中找到研究论文在任何特定领域的贡献是一项挑战。因此,有必要在知识图谱(KG)中对相关贡献进行结构化处理。在本文中,我们介绍了为构建科学知识图谱(SKG)而完成的四项工作。我们提出了一个流水线系统,该系统可执行贡献句识别、贡献句中的短语提取、信息单元(IU)分类,并将短语从 NLP 学术出版物中组织成三联体(主语、谓语、宾语)。我们开发了一个多任务系统(ContriSci),用于识别贡献句,并有两个辅助任务,即章节识别和信息单位分类。我们使用来自变换器的双向编码器表示(BERT)-条件随机场(CRF)模型进行短语提取,并使用两个额外的数据集进行训练:SciERC 和 SciClaim。我们使用基于 BERT 的模型对贡献句子进行 IU 分类。在三连音提取方面,我们将三连音分为五类,并使用基于 BERT 的分类器对三连音进行分类。在非端到端环境下,我们提出的方法在贡献句识别、短语提取、IU 分类和三连音提取方面的 F1 得分值分别为 64.21%、77.47%、84.52% 和 62.71%。在 NLPContributionGraph(NCG)数据集上,贡献句识别、IUs 分类和三连音提取的 F1 分数分别提高了 8.08、2.46 和 2.31。我们的系统在所有四个子任务的端到端流水线中取得了最佳性能(57.54% 的 F1 分数)。我们的代码可在以下网址获取:https://github.com/92Komal/pipeline_triplet_extraction.
A BERT-based sequential deep neural architecture to identify contribution statements and extract phrases for triplets from scientific publications
Research in Natural Language Processing (NLP) is increasing rapidly; as a result, a large number of research papers are being published. It is challenging to find the contributions of the research paper in any specific domain from the huge amount of unstructured data. There is a need for structuring the relevant contributions in Knowledge Graph (KG). In this paper, we describe our work to accomplish four tasks toward building the Scientific Knowledge Graph (SKG). We propose a pipelined system that performs contribution sentence identification, phrase extraction from contribution sentences, Information Units (IUs) classification, and organize phrases into triplets (subject, predicate, object) from the NLP scholarly publications. We develop a multitasking system (ContriSci) for contribution sentence identification with two supporting tasks, viz.Section Identification and Citance Classification. We use the Bidirectional Encoder Representations from Transformers (BERT)—Conditional Random Field (CRF) model for the phrase extraction and train with two additional datasets: SciERC and SciClaim. To classify the contribution sentences into IUs, we use a BERT-based model. For the triplet extraction, we categorize the triplets into five categories and classify the triplets with the BERT-based classifier. Our proposed approach yields the F1 score values of 64.21%, 77.47%, 84.52%, and 62.71% for the contribution sentence identification, phrase extraction, IUs classification, and triplet extraction, respectively, for non-end-to-end setting. The relative improvement for contribution sentence identification, IUs classification, and triplet extraction is 8.08, 2.46, and 2.31 in terms of F1 score for the NLPContributionGraph (NCG) dataset. Our system achieves the best performance (57.54% F1 score) in the end-to-end pipeline with all four sub-tasks combined. We make our codes available at: https://github.com/92Komal/pipeline_triplet_extraction.
期刊介绍:
The International Journal on Digital Libraries (IJDL) examines the theory and practice of acquisition definition organization management preservation and dissemination of digital information via global networking. It covers all aspects of digital libraries (DLs) from large-scale heterogeneous data and information management & access to linking and connectivity to security privacy and policies to its application use and evaluation.The scope of IJDL includes but is not limited to: The FAIR principle and the digital libraries infrastructure Findable: Information access and retrieval; semantic search; data and information exploration; information navigation; smart indexing and searching; resource discovery Accessible: visualization and digital collections; user interfaces; interfaces for handicapped users; HCI and UX in DLs; Security and privacy in DLs; multimodal access Interoperable: metadata (definition management curation integration); syntactic and semantic interoperability; linked data Reusable: reproducibility; Open Science; sustainability profitability repeatability of research results; confidentiality and privacy issues in DLs Digital Library Architectures including heterogeneous and dynamic data management; data and repositories Acquisition of digital information: authoring environments for digital objects; digitization of traditional content Digital Archiving and Preservation Digital Preservation and curation Digital archiving Web Archiving Archiving and preservation Strategies AI for Digital Libraries Machine Learning for DLs Data Mining in DLs NLP for DLs Applications of Digital Libraries Digital Humanities Open Data and their reuse Scholarly DLs (incl. bibliometrics altmetrics) Epigraphy and Paleography Digital Museums Future trends in Digital Libraries Definition of DLs in a ubiquitous digital library world Datafication of digital collections Interaction and user experience (UX) in DLs Information visualization Collection understanding Privacy and security Multimodal user interfaces Accessibility (or "Access for users with disabilities") UX studies