PubMedQA: A Dataset for Biomedical Research Question Answering

Jin, Qiao; Dhingra, Bhuwan; Liu, Zheng­ping; Cohen, William W.; Lu, Xinghua

doi:10.18653/v1/d19-1259

Cited by 209 publications

(173 citation statements)

References 29 publications

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“…Other than for the BERT experiments, all experiments were conducted using the Flair framework [5] which goes on top of Theano providing convenient way to experiment with different combinations of word embeddings. It provides off-the-shelf neural-based system supporting the entity extraction.…”

Section: Entity Extractionmentioning

confidence: 99%

“…We train a Long Short Term Memory (LSTM) network with a hidden state of 256 dimensions, learning rate 0.1, mini-batch size of 8, and is optimized with Adam. We train for 150 epochs, and the model that performs best on the [4] SciELO.org [5] https://github.com/zalandoresearch/flair validation set provided by the organizers of the competition during training is used to prevent overfitting. We were unable to conveniently experiment with BERT embeddings using the Flair framework but preferred the Google Cloud TensorFlow TPU set up for both training contextualized word embeddings and the downstream task fine-tuning and predictions as it works much faster [6] .…”

Section: Entity Extractionmentioning

confidence: 99%

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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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Section: Entity Extractionmentioning

confidence: 99%

Section: Entity Extractionmentioning

confidence: 99%

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

Akhtyamova

Martı́nez

Verspoor

et al. 2020

Preprint

View full text Add to dashboard Cite

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“…It consists of 6,686 questions and explores the i2b2 dataset to generate QA pairs. Recently, [15] released the PubMedQA dataset, where they derive questions based on article titles and answer them with their respective abstracts. Fig.…”

Section: Biomedical Qamentioning

confidence: 99%

Generating Biomedical Question Answering Corpora From Q&A Forums

2020

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Question Answering (QA) is a natural language processing task that aims at obtaining relevant answers to user questions. While some progress has been made in this area, biomedical questions are still a challenge to most QA approaches, due to the complexity of the domain and limited availability of training sets. We present a method to automatically extract question-article pairs from Q&A web forums, which can be used for document retrieval, a crucial step of most QA systems. The proposed framework extracts from selected forums the questions and the respective answers that contain citations. This way, QA systems based on document retrieval can be developed and evaluated using the question-article pairs annotated by users of these forums. We generated the BiQA corpus by applying our framework to three forums, obtaining 7,453 questions and 14,239 question-article pairs. We evaluated how the number of articles associated with each question and the number of votes on each answer affects the performance of baseline document retrieval approaches. Also, we demonstrated that the articles given as answers are significantly similar to the questions and trained a state-of-the-art deep learning model that obtained similar performance to using a dataset manually annotated by experts. The proposed framework can be used to update the BiQA corpus from the same forums as new posts are made, and from other forums that support their answers with documents. The BiQA corpus and the framework used to generate it are available at https://github.com/lasigeBioTM/BiQA.

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“…Extraction of biomedical entities from these narratives is relevant to a number of NLP tasks such as adverse drug and drug-drug interaction extraction [2], [3], biomedical concept normalization, knowledge base population [4], and question answering [5].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

View full text Add to dashboard Cite

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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PubMedQA: A Dataset for Biomedical Research Question Answering

Cited by 209 publications

References 29 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

Generating Biomedical Question Answering Corpora From Q&A Forums

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

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