Annotation of negation in the IULA Spanish Clinical Record Corpus

Marimon, Montserrat; Vivaldi, Jorge; Bel, Núria

doi:10.18653/v1/w17-1807

Cited by 19 publications

(19 citation statements)

References 14 publications

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“…The IULA Spanish Clinical Record (Marimon et al 2017) corpus contains 300 anonymized clinical records from several services of one of the main hospitals in Barcelona (Spain) that was annotated with negation markers and their scopes. It contains 3,194 sentences, out of which 1,093 (34.22%) were annotated with negation cues.…”

Section: Iula Spanish Clinical Recordmentioning

confidence: 99%

Corpora Annotated with Negation: An Overview

Jiménez-Zafra

Morante²,

Martín–Valdivia

et al. 2020

Computational Linguistics

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Negation is a universal linguistic phenomenon with a great qualitative impact on natural language processing applications. The availability of corpora annotated with negation is essential to training negation processing systems. Currently, most corpora have been annotated for English, but the presence of languages other than English on the Internet, such as Chinese or Spanish, is greater every day. In this study, we present a review of the corpora annotated with negation information in several languages with the goal of evaluating what aspects of negation have been annotated and how compatible the corpora are. We conclude that it is very difficult to merge the existing corpora because we found differences in the annotation schemes used, and most importantly, in the annotation guidelines: the way in which each corpus was tokenized and the negation elements that have been annotated. Differently than for other well established tasks like semantic role labeling or parsing, for negation there is no standard annotation scheme nor guidelines, which hampers progress in its treatment.

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Section: Iula Spanish Clinical Recordmentioning

confidence: 99%

Corpora Annotated with Negation: An Overview

Jiménez-Zafra

Morante²,

Martín–Valdivia

et al. 2020

Computational Linguistics

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“…PharmaCoNER: We used SPACCC POS-TAGGER (Soares and gonzalez agirre, 2019) for sentence splitting, word tokenization and POS tagging. We trained FastText embeddings on the following corpora: IBECS (Rodríguez, 2002), IULA-Spanish-English-Corpus (Marimon et al, 2017), MedlinePlus (Miller et al, 2000), PubMed (Lu, 2011), ScIELO (Goldenberg et al, 2007) and PharmaCoNer . We trained embeddings on two variants of corpora: (1) Include train and development set of PharmaCoNER (2) Include complete dataset of PharmaCoNER.…”

Section: Dataset and Experimental Setupmentioning

confidence: 99%

Linguistically Informed Relation Extraction and Neural Architectures for Nested Named Entity Recognition in BioNLP-OST 2019

Gupta¹,

Yaseen²,

Schütze³

2019

Proceedings of the 5th Workshop on BioNLP Open Shared Tasks

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Named Entity Recognition (NER) and Relation Extraction (RE) are essential tools in distilling knowledge from biomedical literature. This paper presents our findings from participating in BioNLP Shared Tasks 2019. We addressed Named Entity Recognition including nested entities extraction, Entity Normalization and Relation Extraction. Our proposed approach of Named Entities can be generalized to different languages and we have shown it's effectiveness for English and Spanish text. We investigated linguistic features, hybrid loss including ranking and Conditional Random Fields (CRF), multi-task objective and tokenlevel ensembling strategy to improve NER. We employed dictionary based fuzzy and semantic search to perform Entity Normalization. Finally, our RE system employed Support Vector Machine (SVM) with linguistic features. Our NER submission (team:MIC-CIS) ranked first in BB-2019 norm+NER task with standard error rate (SER) of 0.7159 and showed competitive performance on PharmaCo NER task with F1-score of 0.8662. Our RE system ranked first in the SeeDev-binary Relation Extraction Task with F1-score of 0.3738.

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“…Oronoz et al (2015) presented an annotated dataset in Spanish for adverse drug reactions analysis. Although the dataset is in Cruz et al (2017) and Marimon et al (2017) annotated negations in Spanish clinical reports.…”

Section: Previous Workmentioning

confidence: 99%

Annotation of Entities and Relations in Spanish Radiology Reports

Cotik

Filippo²,

Roller

et al. 2017

RANLP 2017 - Recent Advances in Natural Language Processing Meet Deep Learning

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Radiology reports express the results of a radiology study and contain information about anatomical entities, findings, measures and impressions of the medical doctor. The use of information extraction techniques can help physicians to access this information in order to understand data and to infer further knowledge.Supervised machine learning methods are very popular to address information extraction, but are usually domain and language dependent. To train new classification models, annotated data is required. Moreover, annotated data is also required as an evaluation resource of information extraction algorithms. However, one major drawback of processing clinical data is the low availability of annotated datasets. For this reason we performed a manual annotation of radiology reports written in Spanish. This paper presents the corpus, the annotation schema, the annotation guidelines and further insight of the data.

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Annotation of negation in the IULA Spanish Clinical Record Corpus

Cited by 19 publications

References 14 publications

Corpora Annotated with Negation: An Overview

Corpora Annotated with Negation: An Overview

Linguistically Informed Relation Extraction and Neural Architectures for Nested Named Entity Recognition in BioNLP-OST 2019

Annotation of Entities and Relations in Spanish Radiology Reports

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