| 1 |
Introducing the HIPE 2022 Shared Task: Named Entity Recognition and Linking in Multilingual Historical Documents
|
|
|
|
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)
|
|
BASE
|
|
Show details
|
|
|
| 2 |
EMBEDDIA tools output example corpus of Estonian, Croatian and Latvian news articles 1.0
|
|
|
|
BASE
|
|
Show details
|
|
|
| 7 |
FiNER-139: A Financial Numeric Entity Recognition Dataset ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 10 |
FiNER-139: A Financial Numeric Entity Recognition Dataset ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 11 |
Analysis of the Full-Size Russian Corpus of Internet Drug Reviews with Complex NER Labeling Using Deep Learning Neural Networks and Language Models
|
|
|
|
In: Applied Sciences; Volume 12; Issue 1; Pages: 491 (2022)
|
|
Abstract:
The paper presents the full-size Russian corpus of Internet users’ reviews on medicines with complex named entity recognition (NER) labeling of pharmaceutically relevant entities. We evaluate the accuracy levels reached on this corpus by a set of advanced deep learning neural networks for extracting mentions of these entities. The corpus markup includes mentions of the following entities: medication (33,005 mentions), adverse drug reaction (1778), disease (17,403), and note (4490). Two of them—medication and disease—include a set of attributes. A part of the corpus has a coreference annotation with 1560 coreference chains in 300 documents. A multi-label model based on a language model and a set of features has been developed for recognizing entities of the presented corpus. We analyze how the choice of different model components affects the entity recognition accuracy. Those components include methods for vector representation of words, types of language models pre-trained for the Russian language, ways of text normalization, and other pre-processing methods. The sufficient size of our corpus allows us to study the effects of particularities of annotation and entity balancing. We compare our corpus to existing ones by the occurrences of entities of different types and show that balancing the corpus by the number of texts with and without adverse drug event (ADR) mentions improves the ADR recognition accuracy with no notable decline in the accuracy of detecting entities of other types. As a result, the state of the art for the pharmacological entity extraction task for the Russian language is established on a full-size labeled corpus. For the ADR entity type, the accuracy achieved is 61.1% by the F1-exact metric, which is on par with the accuracy level for other language corpora with similar characteristics and ADR representativeness. The accuracy of the coreference relation extraction evaluated on our corpus is 71%, which is higher than the results achieved on the other Russian-language corpora.
|
|
Keyword:
adverse drug events; annotated corpus; coreference relation extraction; deep learning; information extraction; language models; machine learning; MESHRUS; named entity recognition; neural networks; pharmacovigilance; social media; UMLS
|
|
URL: https://doi.org/10.3390/app12010491
|
|
BASE
|
|
Hide details
|
|
|
| 12 |
Experiences on the Improvement of Logic-Based Anaphora Resolution in English Texts
|
|
|
|
In: Electronics; Volume 11; Issue 3; Pages: 372 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 13 |
Comparison of Text Mining Models for Food and Dietary Constituent Named-Entity Recognition
|
|
|
|
In: Machine Learning and Knowledge Extraction; Volume 4; Issue 1; Pages: 254-275 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 14 |
A Multi-Entity Knowledge Joint Extraction Method of Communication Equipment Faults for Industrial IoT
|
|
|
|
In: Electronics; Volume 11; Issue 7; Pages: 979 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 15 |
Indirectly Named Entity Recognition ; Reconnaissance d'entités indirectement nommées
|
|
|
|
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)
|
|
BASE
|
|
Show details
|
|
|
| 16 |
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)
|
|
BASE
|
|
Show details
|
|
|
| 17 |
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
|
|
BASE
|
|
Show details
|
|
|
| 18 |
Korpuslinguistik in der Rechtswissenschaft. Eine webbasierte Analyseplattform für EuGH-Entscheidungen ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 19 |
ChemNER: Fine-Grained Chemistry Named Entity Recognition with Ontology-Guided Distant Supervision ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 20 |
MuVER: Improving First-Stage Entity Retrieval with Multi-View Entity Representations ...
|
|
|
|
BASE
|
|
Show details
|
|
|
|
|
