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	title        = {Reducing Complexity in Parsing Scientific Medical Data, a Diabetes Case Study},
	abstract     = {The aim of this study is to assemble and deploy various NLP components and resources in order to parse scientific medical data and evaluate the degree in which these resources contribute to the overall parsing performance. With parsing we limit our efforts to the identi-fication of unrestricted noun phrases with full phrase structure and investigate the effects of using layers of semantic annotations prior to parsing. Scientific medical texts exhibit com-plex linguistic structure but also regularities that can be captured by pre-processing the texts with specialized semantically-aware tools. Our results show evidence of improved performance while the complexity of parsing is reduced. Parsed scientific texts and inferred syntactic information can be leveraged to improve the accuracy of higher-level tasks such as information extraction and enhance the acquisition of semantic relations and events.},
	booktitle    = {Workshop: Biomedical Natural Language Processing in conjunction with Recent Advances in Natural Language Processing (RANLP). Hissar, Bulgaria.},
	author       = {Kokkinakis, Dimitrios},
	year         = {2011},