Evaluation of two dependency parsers on biomedical corpus targeted at protein-protein interactions.
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.
Pyysalo
Sampo
S
Turku Centre for Computer Science (TUCS), Department of Computer Science, University of Turku, Lemminkäisenkatu 14A, 20520 Turku, Finland. sampo.pyysalo@it.utu.fi
Ginter
Filip
F
Pahikkala
Tapio
T
Boberg
Jorma
J
Järvinen
Jouni
J
Salakoski
Tapio
T
D000043:Abstracting and Indexing / Q000379:methods*; D001185:Artificial Intelligence; D003628:Database Management Systems; D016206:Databases, Bibliographic; D016247:Information Storage and Retrieval / Q000379:methods; D009323:Natural Language Processing; D010506:Periodicals as Topic; D025941:Protein Interaction Mapping; D009626:Terminology as Topic; D018875:Vocabulary, Controlled