{"title":"基于cnn的时间关系问题编码器分类框架。","authors":"Yohei Seki, Kangkang Zhao, Masaki Oguni, Kazunari Sugiyama","doi":"10.1007/s00799-021-00310-1","DOIUrl":null,"url":null,"abstract":"<p><p>Temporal-relation classification plays an important role in the field of natural language processing. Various deep learning-based classifiers, which can generate better models using sentence embedding, have been proposed to address this challenging task. These approaches, however, do not work well due to the lack of task-related information. To overcome this problem, we propose a novel framework that incorporates prior information by employing awareness of events and time expressions (time-event entities) with various window sizes to focus on context words around the entities as a filter. We refer to this module as \"question encoder.\" In our approach, this kind of prior information can extract task-related information from simple sentence embedding. Our experimental results on a publicly available <i>Timebank-Dense</i> corpus demonstrate that our approach outperforms some state-of-the-art techniques, including CNN-, LSTM-, and BERT-based temporal relation classifiers.</p>","PeriodicalId":44974,"journal":{"name":"International Journal on Digital Libraries","volume":null,"pages":null},"PeriodicalIF":1.6000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8513567/pdf/","citationCount":"1","resultStr":"{\"title\":\"CNN-based framework for classifying temporal relations with question encoder.\",\"authors\":\"Yohei Seki, Kangkang Zhao, Masaki Oguni, Kazunari Sugiyama\",\"doi\":\"10.1007/s00799-021-00310-1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Temporal-relation classification plays an important role in the field of natural language processing. Various deep learning-based classifiers, which can generate better models using sentence embedding, have been proposed to address this challenging task. These approaches, however, do not work well due to the lack of task-related information. To overcome this problem, we propose a novel framework that incorporates prior information by employing awareness of events and time expressions (time-event entities) with various window sizes to focus on context words around the entities as a filter. We refer to this module as \\\"question encoder.\\\" In our approach, this kind of prior information can extract task-related information from simple sentence embedding. Our experimental results on a publicly available <i>Timebank-Dense</i> corpus demonstrate that our approach outperforms some state-of-the-art techniques, including CNN-, LSTM-, and BERT-based temporal relation classifiers.</p>\",\"PeriodicalId\":44974,\"journal\":{\"name\":\"International Journal on Digital Libraries\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.6000,\"publicationDate\":\"2022-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8513567/pdf/\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal on Digital Libraries\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1007/s00799-021-00310-1\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2021/10/13 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q2\",\"JCRName\":\"INFORMATION SCIENCE & LIBRARY SCIENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal on Digital Libraries","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s00799-021-00310-1","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2021/10/13 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"INFORMATION SCIENCE & LIBRARY SCIENCE","Score":null,"Total":0}
CNN-based framework for classifying temporal relations with question encoder.
Temporal-relation classification plays an important role in the field of natural language processing. Various deep learning-based classifiers, which can generate better models using sentence embedding, have been proposed to address this challenging task. These approaches, however, do not work well due to the lack of task-related information. To overcome this problem, we propose a novel framework that incorporates prior information by employing awareness of events and time expressions (time-event entities) with various window sizes to focus on context words around the entities as a filter. We refer to this module as "question encoder." In our approach, this kind of prior information can extract task-related information from simple sentence embedding. Our experimental results on a publicly available Timebank-Dense corpus demonstrate that our approach outperforms some state-of-the-art techniques, including CNN-, LSTM-, and BERT-based temporal relation classifiers.
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