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Introducing the HIPE 2022 Shared Task: Named Entity Recognition and Linking in Multilingual Historical Documents
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In: Advances in Information Retrieval. 44th European Conference on IR Research, ECIR 2022, Stavanger, Norway, April 10–14, 2022, Proceedings, Part II ; https://hal.archives-ouvertes.fr/hal-03635971 ; Matthias Hagen; Suzan Verberne; Craig Macdonald; Christin Seifert; Krisztian Balog; Kjetil Nørvåg; Vinay Setty. Advances in Information Retrieval. 44th European Conference on IR Research, ECIR 2022, Stavanger, Norway, April 10–14, 2022, Proceedings, Part II, 13186, Springer International Publishing, pp.347-354, 2022, Lecture Notes in Computer Science, 978-3-030-99738-0. ⟨10.1007/978-3-030-99739-7_44⟩ (2022)
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EMBEDDIA tools output example corpus of Estonian, Croatian and Latvian news articles 1.0
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FiNER-139: A Financial Numeric Entity Recognition Dataset ...
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FiNER-139: A Financial Numeric Entity Recognition Dataset ...
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| 11 |
Analysis of the Full-Size Russian Corpus of Internet Drug Reviews with Complex NER Labeling Using Deep Learning Neural Networks and Language Models
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In: Applied Sciences; Volume 12; Issue 1; Pages: 491 (2022)
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| 12 |
Experiences on the Improvement of Logic-Based Anaphora Resolution in English Texts
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In: Electronics; Volume 11; Issue 3; Pages: 372 (2022)
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| 13 |
Comparison of Text Mining Models for Food and Dietary Constituent Named-Entity Recognition
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In: Machine Learning and Knowledge Extraction; Volume 4; Issue 1; Pages: 254-275 (2022)
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A Multi-Entity Knowledge Joint Extraction Method of Communication Equipment Faults for Industrial IoT
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In: Electronics; Volume 11; Issue 7; Pages: 979 (2022)
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Abstract:
The Industrial Internet of Things (IIoT) deploys massive communication devices for information collection and process control. Once it reaches failure, it will seriously affect the operation of the industrial system. This paper proposes a new method for multi-entity knowledge joint extraction (MEKJE) of IIoT communication equipment faults. This method constructs a multi-task tightly coupled model of fault entity and relationship extraction. We use it to implement word embedding and bidirectional semantic capture to generate computable text vectors. At the same time, a multi-entity segmentation method is proposed, which uses noise filtering to distinguish the multi-fault relationship of single corpus. We constructed a dataset of communication failures in power IIoT and conducted experiments. The experimental results show that the method performs best in tests with the Faulty Text dataset and the CLUENER dataset. In particular, the model achieves an F1 value of 78.6% in the evaluation of relationship extraction for multiple entities, and a significant improvement of 5–8% in its accuracy and recall. It enables effective mapping and accurate extraction of fault knowledge data.
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Keyword:
entity recognition; joint learning; knowledge graph; multi-entity segmentation; relationship extraction
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URL: https://doi.org/10.3390/electronics11070979
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| 15 |
Indirectly Named Entity Recognition ; Reconnaissance d'entités indirectement nommées
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In: ISSN: 2530-9455 ; Journal of Computer-Assisted Linguistic Research (JCLR) ; https://hal.archives-ouvertes.fr/hal-03476411 ; Journal of Computer-Assisted Linguistic Research (JCLR), Universitat Politècnica de València, 2021, 5 (1), pp.27-46. ⟨10.4995/JCLR.2021.15922⟩ ; https://polipapers.upv.es/index.php/jclr/index (2021)
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| 16 |
Atténuer les erreurs de numérisation dans la reconnaissance d'entités nommées pour les documents historiques
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In: Conférence en Recherche d'Informations et Applications (CORIA 2021) ; https://hal.archives-ouvertes.fr/hal-03320332 ; Conférence en Recherche d'Informations et Applications (CORIA 2021), ARIA : Association Francophone de Recherche d’Information (RI) et Applications, Apr 2021, Grenoble (virtuel), France. pp.1 - 7 ; http://coria.asso-aria.org/2021/articles/mini_24/main.pdf (2021)
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Exploring Construction of a Company Domain-Specific Knowledge Graph from Financial Texts Using Hybrid Information Extraction
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Jen, Chun-Heng. - : KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021
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Korpuslinguistik in der Rechtswissenschaft. Eine webbasierte Analyseplattform für EuGH-Entscheidungen ...
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ChemNER: Fine-Grained Chemistry Named Entity Recognition with Ontology-Guided Distant Supervision ...
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MuVER: Improving First-Stage Entity Retrieval with Multi-View Entity Representations ...
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