title: A hybrid method for relation extraction from biomedical literature.
abstract: OBJECTIVE
  Over recent years, there has been a growing interest in extracting entities and relations from biomedical literature. There are a vast number of systems and approaches being proposed to extract biological relations, but none of them achieves satisfactory results. These methodologies are either parsing-based or pattern-based, which are not competent to handle the grammatical complexities of biomedical texts, or too complicated to be adapted. It is well known that appositive, coordinative propositions and such grammatical structures are extremely common in biomedical texts, particularly in full texts. However, these problems are still untouched for most of researchers.
  
  
  METHODS
  In this paper, we have proposed a new approach, which is hybrid with both shallow parsing and pattern matching, to extract relations between proteins from scientific papers of biomedical themes. In the method, appositive and coordinative structures are interpreted based on the shallow parsing analysis, with both syntactic and semantic constraints. Then long sentences are splitted into sub-ones, from which relations are extracted by a greedy pattern matching algorithm, along with automatically generated patterns.
  
  
  RESULTS
  Our approach is experimented to extract protein-protein interactions from full biomedical texts, and has achieved an average F-score of 80% on individual verbs, and 66% on all verbs. With the help of shallow parsing analysis, pattern matching is improved remarkably. Compared with the traditional pattern matching algorithm, our approach achieves about 7% improvement of both precision and F-score. In contrast to other systems, our approach achieves performance comparable to the best. A demo system has been available at http://spies.cs.tsinghua.edu.cn.
authors.affiliation: State Key Laboratory of Intelligent Technology and Systems (LITS), Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China.
authors.forename: Minlie
authors.initials: M
authors.lastname: Huang
authors.forename: Xiaoyan
authors.initials: X
authors.lastname: Zhu
authors.forename: Ming
authors.initials: M
authors.lastname: Li
mesh_terms: D000043:Abstracting and Indexing / Q000379:methods*; D001185:Artificial Intelligence; D001695:Biology; D003628:Database Management Systems; D016206:Databases, Bibliographic; D016247:Information Storage and Retrieval / Q000379:methods; D008037:Linguistics; D008511:Medicine; D009323:Natural Language Processing; D010506:Periodicals as Topic; D012660:Semantics; D009626:Terminology as Topic; D018875:Vocabulary, Controlled