Performance of Stanford and Minipar Parser on Biomedical Texts
This work addresses the problem of dependency parsing accuracy for biomedical researchers, but it is incremental as it applies existing methods to new data without proposing improvements.
The paper evaluated the performance of Stanford and Minipar dependency parsers on biomedical texts, finding that both fail to assign dependencies between connected concepts when separated by at least one clause, and Minipar is unsuitable for this domain with measured precision, recall, and F-scores.
In this paper, the performance of two dependency parsers, namely Stanford and Minipar, on biomedical texts has been reported. The performance of te parsers to assignm dependencies between two biomedical concepts that are already proved to be connected is not satisfying. Both Stanford and Minipar, being statistical parsers, fail to assign dependency relation between two connected concepts if they are distant by at least one clause. Minipar's performance, in terms of precision, recall and the F-score of the attachment score (e.g., correctly identified head in a dependency), to parse biomedical text is also measured taking the Stanford's as a gold standard. The results suggest that Minipar is not suitable yet to parse biomedical texts. In addition, a qualitative investigation reveals that the difference between working principles of the parsers also play a vital role for Minipar's degraded performance.