Sortal anaphora resolution to enhance relation extraction from biomedical literature

• Published in: BMC bioinformatics Vol. 17; no. 140; p. 163
• Main Authors: Kilicoglu, Halil, Rosemblat, Graciela, Fiszman, Marcelo, Rindflesch, Thomas C
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 14.04.2016; BioMed Central
• Subjects: Biological Ontologies; Computational linguistics; Databases, Factual; Language processing; Linguistics; Medical literature; Natural language interfaces; Natural Language Processing; Semantics; United States; Sortal anaphora resolution; Natural language processing; Biomedical literature; Semantic relation extraction
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-016-1009-6

Entity coreference is common in biomedical literature and it can affect text understanding systems that rely on accurate identification of named entities, such as relation extraction and automatic summarization. Coreference resolution is a foundational yet challenging natural language processing task which, if performed successfully, is likely to enhance such systems significantly. In this paper, we propose a semantically oriented, rule-based method to resolve sortal anaphora, a specific type of coreference that forms the majority of coreference instances in biomedical literature. The method addresses all entity types and relies on linguistic components of SemRep, a broad-coverage biomedical relation extraction system. It has been incorporated into SemRep, extending its core semantic interpretation capability from sentence level to discourse level. We evaluated our sortal anaphora resolution method in several ways. The first evaluation specifically focused on sortal anaphora relations. Our methodology achieved a F1 score of 59.6 on the test portion of a manually annotated corpus of 320 Medline abstracts, a 4-fold improvement over the baseline method. Investigating the impact of sortal anaphora resolution on relation extraction, we found that the overall effect was positive, with 50 % of the changes involving uninformative relations being replaced by more specific and informative ones, while 35 % of the changes had no effect, and only 15 % were negative. We estimate that anaphora resolution results in changes in about 1.5 % of approximately 82 million semantic relations extracted from the entire PubMed. Our results demonstrate that a heavily semantic approach to sortal anaphora resolution is largely effective for biomedical literature. Our evaluation and error analysis highlight some areas for further improvements, such as coordination processing and intra-sentential antecedent selection.