Distantly Supervised Biomedical Named Entity Recognition with Dictionary Expansion

Wang, Xuan; Li, Qi; Ren, Xiang; Shang, Jingbo

doi:10.1109/bibm47256.2019.8983212

Cited by 19 publications

(11 citation statements)

References 27 publications

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“…To answer these questions, we apply our fine-grained entity extraction system CORD-NER (Wang et al, 2020c) to extract 75 types of entities to enrich the KG, including many COVID-19 specific new entity types (e.g., coronaviruses, viral proteins, evolution, materials, substrates, and immune responses). CORD-NER relies on distantly-and weakly-supervised methods (Wang et al, 2019b;, with no need for expensive human annotation. Its entity annotation quality surpasses SciSpacy (up to 93.95% F-score, over 10% higher on the F1 score based on a sample set of documents), a fully supervised BioNER tool.…”

Section: Fine-grained Text Entity Extractionmentioning

confidence: 99%

COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation

Wang¹,

Li²,

Wang³

et al. 2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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To combat COVID-19, both clinicians and scientists need to digest vast amounts of relevant biomedical knowledge in scientific literature to understand the disease mechanism and related biological functions. We have developed a novel and comprehensive knowledge discovery framework, COVID-KG to extract finegrained multimedia knowledge elements (entities and their visual chemical structures, relations and events) from scientific literature. We then exploit the constructed multimedia knowledge graphs (KGs) for question answering and report generation, using drug repurposing as a case study. Our framework also provides detailed contextual sentences, subfigures, and knowledge subgraphs as evidence. All of the data, KGs, reports 1 , resources, and shared services are publicly available 2 .

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Section: Fine-grained Text Entity Extractionmentioning

confidence: 99%

COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation

Wang¹,

Li²,

Wang³

et al. 2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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“…Aiming to reduce expensive manual annotation, distant supervision has been used to generate training labels automatically by utilizing the entity information from existing KBs. The major research efforts lie in dealing with the incomplete annotation problem caused by an incomplete coverage of the KBs (Fries et al, 2017;Shang et al, 2018b;Peng et al, 2019;Wang et al, 2019aWang et al, , 2020aLiang et al, 2020).…”

Section: Related Workmentioning

confidence: 99%

ChemNER: Fine-Grained Chemistry Named Entity Recognition with Ontology-Guided Distant Supervision

Wang¹,

Hu²,

Song³

et al. 2021

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

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Scientific literature analysis needs fine-grained named entity recognition (NER) to provide a wide range of information for scientific discovery. For example, chemistry research needs to study dozens to hundreds of distinct, fine-grained entity types, making consistent and accurate annotation difficult even for crowds of domain experts. On the other hand, domain-specific ontologies and knowledge bases (KBs) can be easily accessed, constructed, or integrated, which makes distant supervision realistic for fine-grained chemistry NER. In distant supervision, training labels are generated by matching mentions in a document with the concepts in the knowledge bases (KBs). However, this kind of KB-matching suffers from two major challenges: incomplete annotation and noisy annotation. We propose CHEMNER, an ontologyguided, distantly-supervised method for finegrained chemistry NER to tackle these challenges. It leverages the chemistry type ontology structure to generate distant labels with novel methods of flexible KB-matching and ontology-guided multi-type disambiguation. It significantly improves the distant label generation for the subsequent sequence labeling model training. We also provide an expertlabeled, chemistry NER dataset with 62 finegrained chemistry types (e.g., chemical compounds and chemical reactions). Experimental results show that CHEMNER is highly effective, outperforming substantially the stateof-the-art NER methods (with .25 absolute F1 score improvement).

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“…The open information extraction pipeline extracts entities with distant supervision from knowledge bases and relations with automatic meta-pattern discovery methods. In particular, to extract highquality entities and relations, we design noiserobust neural models for distantly supervised named entity recognition (Shang et al, 2018b;Wang et al, 2019) and wide-window meta-pattern discovery methods to deal with the long and complex sentences in biomedical literature (Wang et al, 2018a;. Data Collection.…”

Section: Open Information Extractionmentioning

confidence: 99%

“…To tackle the problem of limited coverage of the input dictionary, we first apply a data-driven phrase mining algorithm, AutoPhrase (Shang et al, 2018a), to extract high-quality phrases as additional entity candidates. Then we automatically expand the dictionary with a novel dictionary expansion method (Wang et al, 2019). The expanded dictionary is used to label the input corpora with the 17 finegrained entity types to train a neural model.…”

Section: Open Information Extractionmentioning

confidence: 99%

“…It is supported by novel data-driven methods for distantly supervised named entity recognition and open information extraction. EVIDENCEMINER relies on external knowledge bases to provide distant supervision for named entity recognition (NER) (Shang et al, 2018b;Wang et al, , 2019. Based on the entity annotation results, it automatically extracts informative meta-patterns (textual patterns containing entity types, e.g., CHEMICAL inhibit DIS-EASE) from sentences in the background corpora.…”

Section: Introductionmentioning

confidence: 99%

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EVIDENCEMINER: Textual Evidence Discovery for Life Sciences

Wang¹,

Guan²,

Liu³

et al. 2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations

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Traditional search engines for life sciences (e.g., PubMed) are designed for document retrieval and do not allow direct retrieval of specific statements. Some of these statements may serve as textual evidence that is key to tasks such as hypothesis generation and new finding validation. We present EVIDENCEM-INER, a web-based system that lets users query a natural language statement and automatically retrieves textual evidence from a background corpora for life sciences. EVIDENCEMINER is constructed in a completely automated way without any human effort for training data annotation. It is supported by novel data-driven methods for distantly supervised named entity recognition and open information extraction. The entities and patterns are pre-computed and indexed offline to support fast online evidence retrieval. The annotation results are also highlighted in the original document for better visualization. EVIDENCEMINER also includes analytic functionalities such as the most frequent entity and relation summarization. EVIDENCEMINER can help scientists uncover essential research issues, leading to more effective research and more indepth quantitative analysis. The system of EVIDENCEMINER is available at https:// evidenceminer.firebaseapp.com/ 1 .

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Distantly Supervised Biomedical Named Entity Recognition with Dictionary Expansion

Cited by 19 publications

References 27 publications

COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation

COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation

ChemNER: Fine-Grained Chemistry Named Entity Recognition with Ontology-Guided Distant Supervision

EVIDENCEMINER: Textual Evidence Discovery for Life Sciences

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