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1
Towards combined semantic and lexical scores based on a new representation of textual data to extract experimental data from scientific publications
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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2
MEduKG: A Deep-Learning-Based Approach for Multi-Modal Educational Knowledge Graph Construction
In: Information; Volume 13; Issue 2; Pages: 91 (2022)
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3
Prosodic Feature-Based Discriminatively Trained Low Resource Speech Recognition System
In: Sustainability; Volume 14; Issue 2; Pages: 614 (2022)
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4
Atténuer les erreurs de numérisation dans la reconnaissance d'entités nommées pour les documents historiques
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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5
LILLIE : information extraction and database integration using linguistics and learning-based algorithms ...
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6
Exploring Construction of a Company Domain-Specific Knowledge Graph from Financial Texts Using Hybrid Information Extraction
Jen, Chun-Heng. - : KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021
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7
Data for Cyrillic Reference Parsing ...
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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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Data for Cyrillic Reference Parsing ...
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11
Automatic Error Type Annotation for Arabic ...
Abstract: We present ARETA, an automatic error type annotation system for Modern Standard Arabic. We design ARETA to address Arabic’s morphological richness and orthographic ambiguity. We base our error taxonomy on the Arabic Learner Corpus (ALC) Error Tagset with some modifications. ARETA achieves a performance of 85.8% (micro average F1 score) on a manually annotated blind test portion of ALC. We also demonstrate ARETA’s usability by applying it to a number of submissions from the QALB 2014 shared task for Arabic grammatical error correction. The resulting analyses give helpful insights on the strengths and weaknesses of different submissions, which is more useful than the opaque M2 scoring metrics used in the shared task. ARETA employs a large Arabic morphological analyzer but is completely unsupervised otherwise. We make ARETA publicly available. ...
Keyword: Computational Linguistics; Information Extraction; Language Models; Machine Learning; Machine Learning and Data Mining; Natural Language Processing
URL: https://dx.doi.org/10.48448/1s26-nw85
https://underline.io/lecture/39891-automatic-error-type-annotation-for-arabic
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12
Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-Training ...
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13
HittER: Hierarchical Transformers for Knowledge Graph Embeddings ...
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14
AttentionRank: Unsupervised Keyphrase Extraction using Self and Cross Attentions ...
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15
Corpus-based Open-Domain Event Type Induction ...
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16
Extracting Event Temporal Relations via Hyperbolic Geometry ...
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17
Partially Supervised Named Entity Recognition via the Expected Entity Ratio Loss ...
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18
Honey or Poison? Solving the Trigger Curse in Few-shot Event Detection via Causal Intervention ...
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19
Corrected CBOW Performs as well as Skip-gram ...
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20
An Empirical Study on Multiple Information Sources for Zero-Shot Fine-Grained Entity Typing ...
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