Biomedical Event Extraction based on Knowledge-driven Tree-

Li, Diya; Huang, Lifu; Ji, Heng

doi:10.18653/v1/n19-1145

Cited by 47 publications

(51 citation statements)

References 34 publications

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“…However, while dealing with more complex biomedical NER problems including long, discontinuous, overlapping entities, hybrid approaches show the best results. Li et al [33] integrated KB embeddings in their tree-structured LSTM framework, achieving approximately 3% gain in F-score.…”

Section: A Biomedical Entity Extraction Approachesmentioning

confidence: 99%

Testing Contextualized Word Embeddings to Improve NER in Spanish Clinical Case Narratives

et al. 2020

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In the Big Data era, there is an increasing need to fully exploit and analyze the huge quantity of information available about health. Natural Language Processing (NLP) technologies can contribute by extracting relevant information from unstructured data contained in Electronic Health Records (EHR) such as clinical notes, patients' discharge summaries and radiology reports. The extracted information can help in health-related decision making processes. The Named Entity Recognition (NER) task, which detects important concepts in texts (e.g., diseases, symptoms, drugs, etc.), is crucial in the information extraction process yet has received little attention in languages other than English. In this work, we develop a deep learning-based NLP pipeline for biomedical entity extraction in Spanish clinical narratives. We explore the use of contextualized word embeddings, which incorporate context variation into word representations, to enhance named entity recognition in Spanish language clinical text, particularly of pharmacological substances, compounds, and proteins. Various combinations of word and sense embeddings were tested on the evaluation corpus of the PharmacoNER 2019 task, the Spanish Clinical Case Corpus (SPACCC). This data set consists of clinical case sections extracted from open access Spanishlanguage medical publications. Our study shows that our deep-learning-based system with domain-specific contextualized embeddings coupled with stacking of complementary embeddings yields superior performance over a system with integrated standard and general-domain word embeddings. With this system, we achieve performance competitive with the state-of-the-art. INDEX TERMS Clinical case narratives, Contextualized word embeddings, Deep learning, Language representations, Named entity recognition, Natural language processing, Spanish language Recent developments in NLP have shown the advantage

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Section: A Biomedical Entity Extraction Approachesmentioning

confidence: 99%

Testing Contextualized Word Embeddings to Improve NER in Spanish Clinical Case Narratives

et al. 2020

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“…Upon the typical Bi-LSTM (bidirectional LSTM), Zhang et al [168] further constructed a Tree-LSTM yet centered at the target word by transforming the original dependency tree of a syntactic dependency analyzer for Chinese event detection. Li et al [169] proposed to further augment a Tree-LSTM with external entity ontological knowledge for biomedical event extraction.…”

Section: B Recurrent Neural Networkmentioning

confidence: 99%

A Survey of Event Extraction From Text

2019

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Numerous important events happen everyday and everywhere but are reported in different media sources with different narrative styles. How to detect whether real-world events have been reported in articles and posts is one of the main tasks of event extraction. Other tasks include extracting event arguments and identifying their roles, as well as clustering and tracking similar events from different texts. As one of the most important research themes in natural language processing and understanding, event extraction has a wide range of applications in diverse domains and has been intensively researched for decades. This article provides a comprehensive yet up-to-date survey for event extraction from text. We not only summarize the task definitions, data sources and performance evaluations for event extraction, but also provide a taxonomy for its solution approaches. In each solution group, we provide detailed analysis for the most representative methods, especially their origins, basics, strengths and weaknesses. Last, we also present our envisions about future research directions. INDEX TERMS Event extraction, event extraction tasks, event corpus, natural language processing.

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“…However, while dealing with more complex biomedical NER problems including long, discontinuous, overlapping entities, hybrid approaches show the best results. Li et al [32] integrated KB embeddings in their tree-structured LSTM framework, achieving approximately 3% gain in F-score.…”

Section: Biomedical Entity Extractionmentioning

confidence: 99%

Testing Contextualized Word Embeddings to Improve NER in Spanish Clinical Case Narratives

Akhtyamova

Martı́nez

Verspoor

et al. 2020

Preprint

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Background: In the Big Data era there is an increasing need to fully exploit and analyse the huge quantity of information available about health. Natural Language Processing (NLP) technologies can contribute to extract relevant information from unstructured data contained in Electronic Health Records (EHR) such as clinical notes, patient’s discharge summaries and radiology reports among others. Extracted information could help in health-related decision making processes. Named entity recognition (NER) devoted to detect important concepts in texts (diseases, symptoms, drugs, etc.) is a crucial task in information extraction processes especially in languages other than English. In this work, we develop a deep learning-based NLP pipeline for biomedical entity extraction in Spanish clinical narrative. Methods: We explore the use of contextualized word embeddings to enhance named entity recognition in Spanish language clinical text, particularly of pharmacological substances, compounds, and proteins. Various combinations of word and sense embeddings were tested on the evaluation corpus of the PharmacoNER 2019 task, the Spanish Clinical Case Corpus (SPACCC). This data set consists of clinical case sections derived from open access Spanish-language medical publications. Results: NER system integrates in-domain pre-trained Flair and FastText word embeddings, byte-pairwise encoded and the bi-LSTM-based character word embeddings. The system yielded the best performance measure with F-score of 90.84%. Error analysis showed that the main source of errors for the best model is the newly detected false positive entities with the half of that amount of errors belonged to longer than the actual ones detected entities. Conclusions: Our study shows that our deep-learning-based system with domain-specific contextualized embeddings coupled with stacking of complementary embeddings yields superior performance over the system with integrated standard and general-domain word embeddings. With this system, we achieve performance competitive with the state-of-the-art.

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Biomedical Event Extraction based on Knowledge-driven Tree-

Cited by 47 publications

References 34 publications

Testing Contextualized Word Embeddings to Improve NER in Spanish Clinical Case Narratives

Testing Contextualized Word Embeddings to Improve NER in Spanish Clinical Case Narratives

A Survey of Event Extraction From Text

Testing Contextualized Word Embeddings to Improve NER in Spanish Clinical Case Narratives

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