Biomedical Event Extraction with Machine Learning (2014)

Biomedical natural language processing (BioNLP) is a subfield of natural\nlanguage processing, an area of computational linguistics concerned with\ndeveloping programs that work with natural language: written texts and\nspeech. Biomedical relation extraction concerns the detection of semantic\nrelations such as protein-protein interactions (PPI) from scientific texts.\nThe aim is to enhance information retrieval by detecting relations between\nconcepts, not just individual concepts as with a keyword search.\n\nIn recent years, events have been proposed as a more detailed alternative\nfor simple pairwise PPI relations. Events provide a systematic, structural\nrepresentation for annotating the content of natural language texts. Events\nare characterized by annotated trigger words, directed and typed arguments\nand the ability to nest other events. For example, the sentence “Protein A\ncauses protein B to bind protein C” can be annotated with the nested event\nstructure CAUSE(A, BIND(B, C)). Converted to such formal representations,\nthe information of natural language texts can be used by computational\napplications. Biomedical event annotations were introduced by the\nBioInfer and GENIA corpora, and event extraction was popularized by the\nBioNLP'09 Shared Task on Event Extraction.\n\nIn this thesis we present a method for automated event extraction, implemented\nas the Turku Event Extraction System (TEES). A unified graph\nformat is defined for representing event annotations and the problem of\nextracting complex event structures is decomposed into a number of independent\nclassification tasks. These classification tasks are solved using SVM\nand RLS classifiers, utilizing rich feature representations built from full dependency\nparsing. Building on earlier work on pairwise relation extraction\nand using a generalized graph representation, the resulting TEES system is\ncapable of detecting binary relations as well as complex event structures.\nWe show that this event extraction system has good performance, reaching\nthe first place in the BioNLP'09 Shared Task on Event Extraction.\n\nSubsequently, TEES has achieved several first ranks in the BioNLP'11 and\nBioNLP'13 Shared Tasks, as well as shown competitive performance in the\nbinary relation Drug-Drug Interaction Extraction 2011 and 2013 shared\ntasks.\n\nThe Turku Event Extraction System is published as a freely available\nopen-source project, documenting the research in detail as well as making\nthe method available for practical applications. In particular, in this thesis\nwe describe the application of the event extraction method to PubMed-scale\ntext mining, showing how the developed approach not only shows good\nperformance, but is generalizable and applicable to large-scale real-world\ntext mining projects.\n\nFinally, we discuss related literature, summarize the contributions of the\nwork and present some thoughts on future directions for biomedical event\nextraction. This thesis includes and builds on six original research publications.\nThe first of these introduces the analysis of dependency parses that\nleads to development of TEES. The entries in the three BioNLP Shared\nTasks, as well as in the DDIExtraction 2011 task are covered in four publications,\nand the sixth one demonstrates the application of the system to\nPubMed-scale text mining.