title: Identifying non-elliptical entity mentions in a coordinated NP with ellipses.
abstract: Named entities in the biomedical domain are often written using a Noun Phrase (NP) along with a coordinating conjunction such as 'and' and 'or'. In addition, repeated words among named entity mentions are frequently omitted. It is often difficult to identify named entities. Although various Named Entity Recognition (NER) methods have tried to solve this problem, these methods can only deal with relatively simple elliptical patterns in coordinated NPs. We propose a new NER method for identifying non-elliptical entity mentions with simple or complex ellipses using linguistic rules and an entity mention dictionary. The GENIA and CRAFT corpora were used to evaluate the performance of the proposed system. The GENIA corpus was used to evaluate the performance of the system according to the quality of the dictionary. The GENIA corpus comprises 3434 non-elliptical entity mentions in 1585 coordinated NPs with ellipses. The system achieves 92.11% precision, 95.20% recall, and 93.63% F-score in identification of non-elliptical entity mentions in coordinated NPs. The accuracy of the system in resolving simple and complex ellipses is 94.54% and 91.95%, respectively. The CRAFT corpus was used to evaluate the performance of the system under realistic conditions. The system achieved 78.47% precision, 67.10% recall, and 72.34% F-score in coordinated NPs. The performance evaluations of the system show that it efficiently solves the problem caused by ellipses, and improves NER performance. The algorithm is implemented in PHP and the code can be downloaded from https://code.google.com/p/medtextmining/.
authors.affiliation: Department of Computer Science Education, Korea University, Republic of Korea. Electronic address: onacloud@gmail.com.
authors.forename: Jeongmin
authors.initials: J
authors.lastname: Chae
authors.affiliation: Department of Computer Science Education, Korea University, Republic of Korea. Electronic address: coolof@korea.ac.kr.
authors.forename: Younghee
authors.initials: Y
authors.lastname: Jung
authors.affiliation: Department of Computer Science Education, Korea University, Republic of Korea. Electronic address: taeminlee@korea.ac.kr.
authors.forename: Taemin
authors.initials: T
authors.lastname: Lee
authors.affiliation: Department of Computer Science Education, Korea University, Republic of Korea. Electronic address: jsy@korea.ac.kr.
authors.forename: Soonyoung
authors.initials: S
authors.lastname: Jung
authors.affiliation: Department of Computer Science Education, Korea University, Republic of Korea. Electronic address: gjcks@korea.ac.kr.
authors.forename: Chan
authors.initials: C
authors.lastname: Huh
authors.affiliation: Department of Computer Science Education, Korea University, Republic of Korea. Electronic address: gillty@korea.ac.kr.
authors.forename: Gilhan
authors.initials: G
authors.lastname: Kim
authors.affiliation: Department of Computer Science Education, Korea University, Republic of Korea. Electronic address: harrykim@korea.ac.kr.
authors.forename: Hyeoncheol
authors.initials: H
authors.lastname: Kim
authors.affiliation: Department of Laboratory Medicine, Asan Medical Center and University of Ulsan, College of Medicine, Republic of Korea. Electronic address: hboh@amc.seoul.kr.
authors.forename: Heungbum
authors.initials: H
authors.lastname: Oh
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D008037:Linguistics; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods; D012984:Software; D018875:Vocabulary, Controlled
keywords: Ellipsis resolution; Named entity recognition; Text mining