A hybrid approach to finding negated and uncertain expressions in biomedical documents

Fujikawa, Kazuki; Seki, Kazuhiko; Uehara, Kuniaki

doi:10.1145/2389672.2389685

Cited by 6 publications

(11 citation statements)

References 18 publications

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“…Algorithm 1 takes advantage of syntactic information and thus would not be effective for ungrammatical input. More in-depth analysis and discussion of the proposed approach are reported elsewhere [20].…”

Section: Resultsmentioning

confidence: 99%

NegFinder: A Web Service for Identifying Negation Signals and Their Scopes

Fujikawa

Seki

Uehara

2013

IPSJ Transactions on Bioinformatics

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More and more biomedical documents are digitally written and stored. To make the most of the rich resources, it is crucial to precisely locate the information pertinent to user's interests. An obstacle in finding information in natural language text is negations, which deny or reverse the meaning of a sentence. This is especially problematic in the biomedical domain since scientific findings and clinical records often contain negated expressions to state negative effects or the absence of symptoms. This paper reports on our work on a hybrid approach to negation identification combining statistical and heuristic approaches and describes an implementation of the approach, named NegFinder, as a Web service.

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Section: Resultsmentioning

confidence: 99%

NegFinder: A Web Service for Identifying Negation Signals and Their Scopes

Fujikawa

Seki

Uehara

2013

IPSJ Transactions on Bioinformatics

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“…The problem of identifying negated contexts in a biomedical text has received much attention from researchers due to its importance in many biomedical information extraction tasks. Several approaches have been proposed to address this problem using traditional methods (e.g., rule-based systems) and learning-based approaches (e.g., conventional ML methods and deep neural networks) [12,[14][15][16][17][18][19][20][21][22][23][24][25][26][27].…”

Section: Related Workmentioning

confidence: 99%

“…Several approaches have been proposed in prior work to address this challenge by exploring various techniques, including rule-based systems [12,[14][15][16][17], conventional machine learning (ML) [18][19][20][21], convolutional neural networks (CNNs) [22], and bidirectional long short-term memories (Bi-LSTMs) [23][24][25]. Recent successes in the adaptation of transformer-based learning using bidirectional encoder representation from transformers (BERT) [26] for a wide range of language understanding tasks [27][28][29][30] have motivated us to consider such learner models for our negation detection task.…”

Section: Introductionmentioning

confidence: 99%

Exploring Transformer-Based Learning for Negation Detection in Biomedical Texts

Althari

Alsulmi

2022

IEEE Access

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NLP techniques have been widely adopted in the biomedical domain to perform various textanalytics tasks, such as searching biomedical literature and extracting and deriving new knowledge from biomedical data. One type of biomedical data is clinical texts (e.g., clinical cases and medical records), which typically contain physicians' notes about a patient's health, including previous medical history (symptoms, diseases, lab exams, treatments, etc.), as every visit to the hospital leads to the addition of more information to the patient's record. Another type of biomedical data is biological articles, which typically discuss and explore a certain phenomenon, such as the behavior of biological entities (e.g., genetic relations and interactions among them) and the roles of specific biological processes in causing diseases (e.g., how genetic amplification can cause tumorous diseases). For both types of biomedical data, negation detection is an essential analytics task that can be applied to identify negated contexts in biomedical text (e.g., detecting the presence of a statement establishing that a patient does not have/fit a certain clinical condition or detecting statements that indicate the nonexistence of certain relations among biological entities). This task has been addressed in prior work by considering a variety of approaches such as rule-based systems, conventional machine-learning classifiers, and deep learning approaches. In this work, we propose applying transformerbased learning for negation detection in biomedical texts. We use pre-trained BERT and other similar models (such as ALBERT, XLNet, and ELECTRA) to address two negation-detection subtasks: negation sentence identification and negation scope recognition. We evaluated our approach using the BioScope corpus and relying on measures such as accuracy, precision, recall, F1, and percentage of correct scopes (PCS). Our findings show the potential of transformer-based learning for negation detection, reaching an accuracy of 99% for negation identification and a PCS of 95% for negation scope recognition.INDEX TERMS Health informatics, biomedical text analytics, machine learning, natural language processing, text classification.

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“…The rule based NegEx (F measures: 0.89) and NegExpander (F measures: 0.91) outperformed the other two machine learning approaches (F measures: 0.78, 0.86). Several machine learning based or hybrid systems were also explored [4,[12][13][14][15][16]. Only NegClue [15] (a negation detector) and Cogley, et al's system [16] (a temporality and experiencer detector) outperformed the rule-based systems.…”

Section: Context Detectorsmentioning

confidence: 99%