The choice of features for classification of verbs in biomedical texts

Korhonen, Anna; Krymolowski, Yuval; Collier, Nigel

doi:10.3115/1599081.1599138

Cited by 9 publications

(12 citation statements)

References 15 publications

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“…The best techniques have been applied successfully to domains such as biomedicine [21] and they have produced promising results with demonstrated improvement on application tasks such as argumentative zoning [22] and metaphor identification [23].…”

Section: Introductionmentioning

confidence: 99%

Verb Clustering for Brazilian Portuguese

Scarton

Sun

Kipper-Schuler

et al. 2014

Computational Linguistics and Intelligent Text Processing

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Abstract. Levin-style classes which capture the shared syntax and semantics of verbs have proven useful for many Natural Language Processing (NLP) tasks and applications. However, lexical resources which provide information about such classes are only available for a handful of worlds languages. Because manual development of such resources is extremely time consuming and cannot reliably capture domain variation in classification, methods for automatic induction of verb classes from texts have gained popularity. However, to date such methods have been applied to English and a handful of other, mainly resource-rich languages. In this paper, we apply the methods to Brazilian Portuguese -a language for which no VerbNet or automatic class induction work exists yet. Since Levinstyle classification is said to have a strong cross-linguistic component, we use unsupervised clustering techniques similar to those developed for English without language-specific feature engineering. This yields interesting results which line up well with those obtained for other languages, demonstrating the crosslinguistic nature of this type of classification. However, we also discover and discuss issues which require specific consideration when aiming to optimise the performance of verb clustering for Brazilian Portuguese and other less-resourced languages.

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Section: Introductionmentioning

confidence: 99%

Verb Clustering for Brazilian Portuguese

Scarton

Sun

Kipper-Schuler

et al. 2014

Computational Linguistics and Intelligent Text Processing

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“…Such grouping can reduce the parameters used for representing verbs individually. While it is time-consuming to manually classify a large number of verbs, previous studies have shown that it is possible to automatically acquire verb classes from both general [32–35] and biomedical texts [15, 36, 37]. For example, Li and Brew (2008) classify 1,300 verbs into 48 Levin classes using Bayesian Multinomial Regression for classification [38].…”

Section: Related Workmentioning

confidence: 99%

A neural classification method for supporting the creation of BioVerbNet

et al. 2019

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BackgroundVerbNet, an extensive computational verb lexicon for English, has proved useful for supporting a wide range of Natural Language Processing tasks requiring information about the behaviour and meaning of verbs. Biomedical text processing and mining could benefit from a similar resource. We take the first step towards the development of BioVerbNet: A VerbNet specifically aimed at describing verbs in the area of biomedicine. Because VerbNet-style classification is extremely time consuming, we start from a small manual classification of biomedical verbs and apply a state-of-the-art neural representation model, specifically developed for class-based optimization, to expand the classification with new verbs, using all the PubMed abstracts and the full articles in the PubMed Central Open Access subset as data.ResultsDirect evaluation of the resulting classification against BioSimVerb (verb similarity judgement data in biomedicine) shows promising results when representation learning is performed using verb class-based contexts. Human validation by linguists and biologists reveals that the automatically expanded classification is highly accurate. Including novel, valid member verbs and classes, our method can be used to facilitate cost-effective development of BioVerbNet.ConclusionThis work constitutes the first effort on applying a state-of-the-art architecture for neural representation learning to biomedical verb classification. While we discuss future optimization of the method, our promising results suggest that the automatic classification released with this article can be used to readily support application tasks in biomedicine.Electronic supplementary materialThe online version of this article (10.1186/s13326-018-0193-x) contains supplementary material, which is available to authorized users.

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“…This evaluation is less influenced by structural differences between two clustering solutions and allows to quantify results in terms of precision and recall. We also calculated APP Adjusted Pairwise Precision [17]: this measure quantifies average cluster purity, weighted by the size of the clusters. This provides additional information on the proportion of the relevant clusters.…”

Section: Baseline and Evaluation Measuresmentioning

confidence: 99%

Unsupervised Relation Extraction in Specialized Corpora Using Sequence Mining

Gábor

Zargayouna

Buscaldi

et al. 2016

Lecture Notes in Computer Science

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International audienceThis paper deals with the extraction of semantic relations from scientific texts. Pattern-based representations are compared to word embeddings in unsupervised clustering experiments, according to their potential to discover new types of semantic relations and recognize their instances. The results indicate that sequential pattern mining can significantly improve pattern-based representations, even in a completely unsupervised setting

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The choice of features for classification of verbs in biomedical texts

Cited by 9 publications

References 15 publications

Verb Clustering for Brazilian Portuguese

Verb Clustering for Brazilian Portuguese

A neural classification method for supporting the creation of BioVerbNet

Unsupervised Relation Extraction in Specialized Corpora Using Sequence Mining

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