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Towards combined semantic and lexical scores based on a new representation of textual data to extract experimental data from scientific publications
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In: ISSN: 1751-5858 ; EISSN: 1751-5866 ; International Journal of Intelligent Information and Database Systems ; https://hal.inrae.fr/hal-03616243 ; International Journal of Intelligent Information and Database Systems, Inderscience, 2022, 15 (1), pp.78. ⟨10.1504/IJIIDS.2022.120146⟩ (2022)
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MEduKG: A Deep-Learning-Based Approach for Multi-Modal Educational Knowledge Graph Construction
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In: Information; Volume 13; Issue 2; Pages: 91 (2022)
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Prosodic Feature-Based Discriminatively Trained Low Resource Speech Recognition System
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In: Sustainability; Volume 14; Issue 2; Pages: 614 (2022)
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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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LILLIE : information extraction and database integration using linguistics and learning-based algorithms ...
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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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Data for Training and Evaluating Metadata Extraction Models based on 15 Thousand Cyrillic Script Publications ...
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Data for Training and Evaluating Metadata Extraction Models based on 15 Thousand Cyrillic Script Publications ...
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Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-Training ...
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HittER: Hierarchical Transformers for Knowledge Graph Embeddings ...
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AttentionRank: Unsupervised Keyphrase Extraction using Self and Cross Attentions ...
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Corpus-based Open-Domain Event Type Induction ...
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Abstract:
Anthology paper link: https://aclanthology.org/2021.emnlp-main.441/ Abstract: Traditional event extraction methods require predefined event types and their corresponding annotations to learn event extractors. These prerequisites are often hard to be satisfied in real-world applications. This work presents a corpus-based open-domain event type induction method that automatically discovers a set of event types from a given corpus. As events of the same type could be expressed in multiple ways, we propose to represent each event type as a cluster of pairs. Specifically, our method (1) selects salient predicates and object heads, (2) disambiguates predicate senses using only a verb sense dictionary, and (3) obtains event types by jointly embedding and clustering pairs in a latent spherical space. Our experiments, on three datasets from different domains, show our method can discover salient and high-quality event types, according to both ...
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Keyword:
Computational Linguistics; Information Extraction; Machine Learning; Machine Learning and Data Mining; Natural Language Processing
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URL: https://underline.io/lecture/38009-corpus-based-open-domain-event-type-induction https://dx.doi.org/10.48448/zmp7-8083
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Extracting Event Temporal Relations via Hyperbolic Geometry ...
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Partially Supervised Named Entity Recognition via the Expected Entity Ratio Loss ...
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Honey or Poison? Solving the Trigger Curse in Few-shot Event Detection via Causal Intervention ...
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An Empirical Study on Multiple Information Sources for Zero-Shot Fine-Grained Entity Typing ...
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