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ME-based biomedical named entity recognition using lexical knowledge
Kyung-Mi Park, Seon-Ho Kim, Hae-Chang Rim, Young-Sook Hwang
In this paper, we present a two-phase biomedical NE-recognition method based on a ME model: we first recognize biomedical terms and then assign appropriate semantic classes to the recognized terms. In the two-phase NE-recognition method, the...
Mining semantically related terms from biomedical literature
Goran Nenadić, Sophia Ananiadou
Discovering links and relationships is one of the main challenges in biomedical research, as scientists are interested in uncovering entities that have similar functions, take part in the same processes, or are coregulated. This article discusses the...
Extracting contrastive information from negation patterns in biomedical literature
Jung-Jae Kim, Jong C. Park
Expressions of negation in the biomedical literature often encode information of contrast as a means for explaining significant differences between the objects that are so contrasted. We show that such information gives additional insights into the...
Two-phase learning for biological event extraction and verification
Eunju Kim, Yu Song, Cheongjae Lee, Kyoungduk Kim, Gary Geunbae Lee, Byoung-Kee Yi, Jeongwon Cha
Many previous biological event-extraction systems were based on hand-crafted rules which were specifically tuned to a specific biological application domain. But manually constructing and tuning the rules are time-consuming processes and make the...
Terminology-based knowledge mining for new knowledge discovery
Hideki Mima, Sophia Ananiadou, Katsumori Matsushima
In this article we present an integrated knowledge-mining system for the domain of biomedicine, in which automatic term recognition, term clustering, information retrieval, and visualization are combined. The primary objective of this system is to...