Learning the scope of negation in biomedical texts

Morante, Roser; Liekens, Anthony; Daelemans, Walter

doi:10.3115/1613715.1613805

Cited by 64 publications

(70 citation statements)

References 21 publications

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“…Most of this work focuses on supervised classification on the (English) BioScope corpus (Szarvas et al 2008), such as Morante et al (2008) or Zou et al (2013). The approach which is mostly related to ours is, however, the descriptive work by Morante (2010) who analyzes the individual negation words within the BioScope corpus and their scopes.…”

Section: Related Workmentioning

confidence: 99%

Negation Modeling for German Polarity Classification

Wiegand

Wolf

Ruppenhofer

2018

Lecture Notes in Computer Science

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Abstract. We present an approach for modeling German negation in open-domain fine-grained sentiment analysis. Unlike most previous work in sentiment analysis, we assume that negation can be conveyed by many lexical units (and not only common negation words) and that different negation words have different scopes. Our approach is examined on a new dataset comprising sentences with mentions of polar expressions and various negation words. We identify different types of negation words that have the same scopes. We show that already negation modeling based on these types largely outperforms traditional negation models which assume the same scope for all negation words and which employ a window-based scope detection rather than a scope detection based on syntactic information.

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Section: Related Workmentioning

confidence: 99%

Negation Modeling for German Polarity Classification

Wiegand

Wolf

Ruppenhofer

2018

Lecture Notes in Computer Science

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“…For example, Goldin and Chapman [35] utilized Naï ve Bayes and decision tree algorithms to figure out sentences including medical observations negated by the word "not" and sentences that similarly include the word "not" without any negations. Morante et al [36] utilized k-nearest neighbor classifiers to determine the scope of negation. Rokach et al [37] designed an algorithm for learning regular expressions using the longest common subsequence algorithm followed by decision tree classification.…”

Section: Related Workmentioning

confidence: 99%

“…Agarwal and Yu [39] developed the NegScope algorithm that utilizes conditional random fields to detect negations, but it had a lower performance in comparison to NegEx. Fujikawa et al [40] developed the NegFinder algorithm for determining the scope of negation cues by adding syntactic information to the algorithm of Morante et al [36,38] that utilizes k-nearest neighbor classification as discussed above. This algorithm should not be mistaken for the NegFinder algorithm by Mutalik et al [25].…”

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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“…The first relevant task is to identify negation signals and their scopes (e.g., Morante and Daelemans, 2008;2009;Farkas et al, 2010;Agarwal et al, 2011). Manually-annotated corpora like BioScope (Szarvas et al, 2008) were created to annotate negations and their scopes in biomedical abstracts in support of automated identification.…”

Section: Related Workmentioning

confidence: 99%

Classifying Negative Findings in Biomedical Publications

Fanelli

2014

Proceedings of BioNLP 2014

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Publication bias refers to the phenomenon that statistically significant, "positive" results are more likely to be published than non-significant, "negative" results. Currently, researchers have to manually identify negative results in a large number of publications in order to examine publication biases. This paper proposes an NLP approach for automatically classifying negated sentences in biomedical abstracts as either reporting negative findings or not. Using multinomial naïve Bayes algorithm and bag-ofwords features enriched by parts-ofspeeches and constituents, we built a classifier that reached 84% accuracy based on 5-fold cross validation on a balanced data set.

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Learning the scope of negation in biomedical texts

Cited by 64 publications

References 21 publications

Negation Modeling for German Polarity Classification

Negation Modeling for German Polarity Classification

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

Classifying Negative Findings in Biomedical Publications

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