Recently, extraction systems have also used link grammar to identify interactions between proteins. In this chapter, the authors present a fully automated system to extract biomolecular events from biomedical abstracts. By semantically classifying each sentence to the class type of the event and then using high-coverage rules, BioEve extracts the participants of that event. The chapter explains in detail different classification approaches, and event extraction using a dependency parse tree of the sentence is explained here. It describes experiments with classification approaches, event extraction, and evaluation results for the BioNLP'09 shared task 1.
|Original language||English (US)|
|Title of host publication||Biological Knowledge Discovery Handbook|
|Subtitle of host publication||Preprocessing, Mining and Postprocessing of Biological Data|
|Number of pages||25|
|State||Published - 2014|
ASJC Scopus subject areas
- Computer Science(all)