Evaluation of two dependency parsers on biomedical corpus targeted at protein–protein interactions

• Published in: International journal of medical informatics (Shannon, Ireland) Ireland), 2006-06, Vol.75 (6), p.430-442
• Main Authors: Pyysalo, Sampo, Ginter, Filip, Pahikkala, Tapio, Boberg, Jorma, Järvinen, Jouni, Salakoski, Tapio
• Format: Article
• Language: English
• Subjects: Abstracting and Indexing as Topic - methods; Artificial Intelligence; Database Management Systems; Databases, Bibliographic; Dependency syntax; Evaluation; Information Storage and Retrieval - methods; Natural Language Processing; Parser comparison; Periodicals as Topic; Protein Interaction Mapping; Protein–protein interactions; Terminology as Topic; Vocabulary, Controlled
• ISSN: 1386-5056; 1872-8243
• DOI: 10.1016/j.ijmedinf.2005.06.009

We present an evaluation of Link Grammar and Connexor Machinese Syntax, two major broad-coverage dependency parsers, on a custom hand-annotated corpus consisting of sentences regarding protein–protein interactions. In the evaluation, we apply the notion of an interaction subgraph, which is the subgraph of a dependency graph expressing a protein–protein interaction. We measure the performance of the parsers for recovery of individual dependencies, fully correct parses, and interaction subgraphs. For Link Grammar, an open system that can be inspected in detail, we further perform a comprehensive failure analysis, report specific causes of error, and suggest potential modifications to the grammar. We find that both parsers perform worse on biomedical English than previously reported on general English. While Connexor Machinese Syntax significantly outperforms Link Grammar, the failure analysis suggests specific ways in which the latter could be modified for better performance in the domain.