Artif Intell Med - Biomedical events extraction using the hidden vector state model.

Tópicos

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Resumo

JECTIVE: Biomedical events extraction concerns about events describing changes on the state of bio-molecules from literature. Comparing to the protein-protein interactions (PPIs) extraction task which often only involves the extraction of binary relations between two proteins, biomedical events extraction is much harder since it needs to deal with complex events consisting of embedded or hierarchical relations among proteins, events, and their textual triggers. In this paper, we propose an information extraction system based on the hidden vector state (HVS) model, called HVS-BioEvent, for biomedical events extraction, and investigate its capability in extracting complex events.METHODS AND MATERIAL: HVS has been previously employed for extracting PPIs. In HVS-BioEvent, we propose an automated way to generate abstract annotations for HVS training and further propose novel machine learning approaches for event trigger words identification, and for biomedical events extraction from the HVS parse results.RESULTS: Our proposed system achieves an F-score of 49.57% on the corpus used in the BioNLP'09 shared task, which is only 2.38% lower than the best performing system by UTurku in the BioNLP'09 shared task. Nevertheless, HVS-BioEvent outperforms UTurku's system on complex events extraction with 36.57% vs. 30.52% being achieved for extracting regulation events, and 40.61% vs. 38.99% for negative regulation events.CONCLUSIONS: The results suggest that the HVS model with the hierarchical hidden state structure is indeed more suitable for complex event extraction since it could naturally model embedded structural context in sentences.

Resumo Limpo

jectiv biomed event extract concern event describ chang state biomolecul literatur compar proteinprotein interact ppis extract task often involv extract binari relat two protein biomed event extract much harder sinc need deal complex event consist embed hierarch relat among protein event textual trigger paper propos inform extract system base hidden vector state hvs model call hvsbioevent biomed event extract investig capabl extract complex eventsmethod materi hvs previous employ extract ppis hvsbioevent propos autom way generat abstract annot hvs train propos novel machin learn approach event trigger word identif biomed event extract hvs pars resultsresult propos system achiev fscore corpus use bionlp share task lower best perform system uturku bionlp share task nevertheless hvsbioevent outperform uturkus system complex event extract vs achiev extract regul event vs negat regul eventsconclus result suggest hvs model hierarch hidden state structur inde suitabl complex event extract sinc natur model embed structur context sentenc

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