Automatic classification of verbs in biomedical texts

Korhonen, Anna; Krymolowski, Yuval; Collier, Nigel

doi:10.3115/1220175.1220219

Cited by 14 publications

(21 citation statements)

References 17 publications

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“…The three levels reflect different granularity in the semantics of the verb classes as illustrated in Figure 1. These clusters were annotated by 4 domain experts and 2 linguists, were used to create the gold standard (Korhonen Figure 1: Examples of the verb classes introduced by Korhonen et al (2006). et al, 2006).…”

Section: Verb Clustersmentioning

confidence: 99%

Enhancing biomedical word embeddings by retrofitting to verb clusters

Chiu

Baker

Palmer

et al. 2019

Proceedings of the 18th BioNLP Workshop and Shared Task

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Verbs play a fundamental role in many biomedical tasks and applications such as relation and event extraction. We hypothesize that performance on many downstream tasks can be improved by aligning the input pretrained embeddings according to semantic verb classes. In this work, we show that by using semantic clusters for verbs, a large lexicon of verb classes derived from biomedical literature, we are able to improve the performance of common pretrained embeddings in downstream tasks by retrofitting them to verb classes. We present a simple and computationally efficient approach using a widely-available "off-theshelf" retrofitting algorithm to align pretrained embeddings according to semantic verb clusters. We achieve state-of-the-art results on text classification and relation extraction tasks.

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Section: Verb Clustersmentioning

confidence: 99%

Enhancing biomedical word embeddings by retrofitting to verb clusters

Chiu

Baker

Palmer

et al. 2019

Proceedings of the 18th BioNLP Workshop and Shared Task

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“…Next, to obtain as comprehensive subcategorization frequency information as possible, up to 10,000 sentences containing an occurrence of each of these verbs were included in the input data for subcategorization acquisition. These sentences were extracted from five different corpora, including BNC (Korhonen et al, 2006). We used these data to calculate similarity between verbs.…”

Section: Subcategorization Informationmentioning

confidence: 99%

Semantic Classification of Unknown Words Based on Graph-Based Semi-Supervised Clustering

Fukumoto

Suzuki

2011

Proceedings of the International Conference on Knowledge Engineering and Ontology Development

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This paper presents a method for semantic classification of unknown verbs including polysemies into Levinstyle semantic classes. We propose a semi-supervised clustering, which is based on a graph-based unsupervised clustering technique. The algorithm detects the spin configuration that minimizes the energy of the spin glass. Comparing global and local minima of an energy function, called the Hamiltonian, allows for the detection of nodes with more than one cluster. We extended the algorithm so as to employ a small amount of labeled data to aid unsupervised learning, and applied the algorithm to cluster verbs including polysemies. The distributional similarity between verbs used to calculate the Hamiltonian is in the form of probability distributions over verb frames. The result obtained using 110 test polysemous verbs with labeled data of 10% showed 0.577 F-score.

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“…As this pilot study showed that fine-grained domain-specific semantic patterns for verbs can be obtained automatically, we would like to port the approach to a domain where fine-grained typing is of paramount importance. This is the case with the biomedical domain, where for instance verbs of biological interaction, such as inhibit or activate are semantically underspecified Korhonen et al, 2006). However, the specific biological interactions come only through the details of the actual arguments participating in the interaction .…”

Section: Summary and Future Workmentioning

confidence: 99%

Automatic fine-grained semantic classification for domain adaptation

Liakata

Pulman

2008

Proceedings of the 2008 Conference on Semantics in Text Processing - STEP '08

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Assigning arguments of verbs to different semantic classes ('semantic typing'), or alternatively, checking the 'selectional restrictions' of predicates, is a fundamental component of many natural language processing tasks. However, a common experience has been that general purpose semantic classes, such as those encoded in resources like WordNet, or handcrafted subject-specific ontologies, are seldom quite right when it comes to analysing texts from a particular domain. In this paper we describe a method of automatically deriving fine-grained, domain-specific semantic classes of arguments while simultaneously clustering verbs into semantically meaningful groups: the first step in verb sense induction. We show that in a small pilot study on new examples from the same domain we are able to achieve almost perfect recall and reasonably high precision in the semantic typing of verb arguments in these texts.

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Automatic classification of verbs in biomedical texts

Cited by 14 publications

References 17 publications

Enhancing biomedical word embeddings by retrofitting to verb clusters

Enhancing biomedical word embeddings by retrofitting to verb clusters

Semantic Classification of Unknown Words Based on Graph-Based Semi-Supervised Clustering

Automatic fine-grained semantic classification for domain adaptation

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