Identifying non-elliptical entity mentions in a coordinated NP with ellipses

Jeongmin Chae, Younghee Jung, Taemin Lee, Soonyoung Jung, Chan Huh, Gilhan Kim, Hyeoncheol Kim, Heungbum Oh

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)


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

Original languageEnglish
Pages (from-to)139-152
Number of pages14
JournalJournal of Biomedical Informatics
Publication statusPublished - 2014 Feb

Bibliographical note

Funding Information:
This work was supported by ACE program through the National Research Foundation of Korea (NRF) grant funded by the Korea Ministry of Education, Science and Technology (MEST) (No. 20100029093).


  • Ellipsis resolution
  • Named entity recognition
  • Text mining

ASJC Scopus subject areas

  • Health Informatics
  • Computer Science Applications


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