Inferring the Scope of Negation in Biomedical Documents

Ballesteros, Miguel; Francisco, Virginia; Díaz, Alberto; Herrera, Jesús; Gervás, Pablo

doi:10.1007/978-3-642-28604-9_30

Cited by 16 publications

(20 citation statements)

References 9 publications

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“…They can be roughly categorized into manually crafted rule-based approaches [2,3,5,9] and supervised classification-based [1,6,7,16]. The former looks at lexico-syntactic patterns peculiar to negated expressions so as to spot them.…”

Section: Related Workmentioning

confidence: 99%

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

Fujikawa

Seki

Uehara

2012

Proceedings of the 2nd International Workshop on Managing Interoperability and compleXity in Health Systems

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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 users' interests. One of the obstacles 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. Ignoring such negated expressions result in more irrelevant information and may even lead to false conclusions. Therefore, identifying negative words and their scopes are important sub-tasks in biomedical information processing. This paper reports on our ongoing work on a hybrid approach to negation identification combining statistical and heuristic approaches. Our approach is evaluated on three types of biomedical documents in comparison with an existing machine learning approach. In addition, the empirical results are manually analyzed to better understand the nature of the problems.

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Section: Related Workmentioning

confidence: 99%

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

Fujikawa

Seki

Uehara

2012

Proceedings of the 2nd International Workshop on Managing Interoperability and compleXity in Health Systems

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“…This algorithm has been utilized [26] for finding encounter-based events in clinical electronic medical records and for classifying them. The idea of using syntactic and semantic processing for determining the scope of negation cues has been tackled in many other research studies [27][28][29][30][31][32][33][34].…”

Section: Related Workmentioning

confidence: 99%

NegMiner: An Automated Tool for Mining Negations from Electronic Narrative Medical Documents

Elazhary¹

2017

IJISA

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Abstract-Mining negations from electronic narrative medical documents is one of the prominent data mining applications. Since medical documents are freely written, it is impossible to consider all possible sentence structures in advance and so frequent update of mining algorithms is inevitable. Unfortunately most of the proposed algorithms in the literature are too complex to be easily updated. Besides, most of them cannot be easily ported to other natural languages. The simple NegEx algorithm utilizes only two regular expressions and sets of terms to mine negations from narrative medical documents and so does not suffer from these shortcomings. Meanwhile, it has shown impressive mining results and so it is the most widely adopted algorithm. This paper proposes the Negation Mining (NegMiner) tool to address some of the shortcomings of the NegEx algorithm. The NegMiner exploits some basic syntactic and semantic information to deal with contiguous and multiple negations. It is a user-friendly tool that facilitates the task of knowledge base update and the task of document analysis through the use of PDF files. This also makes it able to deal with the existence of a medical finding several times in a single sentence. Experimental results have shown the superiority of the mining results of the NegMiner in comparison to the simulated NegEx algorithm.

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“…It is important to note that the main source of error here is the NegatedPredicate-to-BioScopeScopeSpan trans- Table 3: PCS measures from previous BioScope span detection approaches and our end-to-end system. Col. 1-3: end-to-end systems (Morante and Daelemans, 2009), (Ballesteros et al, 2012), and (Velldal et al, 2012);…”

Section: Our Negatedpredicate Predictormentioning

confidence: 99%

Learning Structures of Negations from Flat Annotations

Prabhakaran

Boguraev²

2015

Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics

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We propose a novel method to learn negation expressions in a specialized (medical) domain. In our corpus, negations are annotated as 'flat' text spans. This allows for some infelicities in the mark-up of the ground truth, making it less than perfectly aligned with the underlying syntactic structure. Nonetheless, the negations thus captured are correct in intent, and thus potentially valuable. We succeed in training a model for detecting the negated predicates corresponding to the annotated negations, by re-mapping the corpus to anchor its 'flat' annotation spans into the predicate argument structure. Our key idea-re-mapping the negation instance spans to more uniform syntactic nodes-makes it possible to re-frame the learning task as a simpler one, and to leverage an imperfect resource in a way which enables us to learn a high performance model. We achieve high accuracy for negation detection overall, 87%. Our re-mapping scheme can be constructively applied to existing flatly annotated resources for other tasks where syntactic context is vital.

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Inferring the Scope of Negation in Biomedical Documents

Cited by 16 publications

References 9 publications

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

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

NegMiner: An Automated Tool for Mining Negations from Electronic Narrative Medical Documents

Learning Structures of Negations from Flat Annotations

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