Use of morphological analysis in protein name recognition.
Protein name recognition aims to detect each and every protein names appearing in a PubMed abstract. The task is not simple, as the graphic word boundary (space separator) assumed in conventional preprocessing does not necessarily coincide with the protein name boundary. Such boundary disagreement caused by tokenization ambiguity has usually been ignored in conventional preprocessing of general English. In this paper, we argue that boundary disagreement poses serious limitations in biomedical English text processing, not to mention protein name recognition. Our key idea for dealing with the boundary disagreement is to apply techniques used in Japanese morphological analysis where there are no word boundaries. Having evaluated the proposed method with GENIA corpus 3.02, we obtain F-measure of 69.01 on a strict criterion and 79.32 on a relaxed criterion. The result is comparable to other published work in protein name recognition, without resorting to manually prepared ad hoc feature engineering. Further, compared to the conventional preprocessing, the use of morphological analysis as preprocessing improves the performance of protein name recognition and reduces the execution time.
Yamamoto
Kaoru
K
CREST, Japan Science and Technology Agency, Japan. kaoru@lr.pi.titech.ac.jp
Kudo
Taku
T
Konagaya
Akihiko
A
Matsumoto
Yuji
Y
D000043:Abstracting and Indexing / Q000379:methods*; D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D016206:Databases, Bibliographic; D030541:Databases, Genetic; D030562:Databases, Protein; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D008390:Markov Chains; D015233:Models, Statistical; D009275:Names; D009323:Natural Language Processing; D011506:Proteins / Q000737:chemistry*; D012984:Software
D011506:Proteins