Integrating machine learning with linguistic features: A universal method for extraction and normalization of temporal expressions in Chinese texts.

Journal: Computer methods and programs in biomedicine
Published Date:

Abstract

BACKGROUND AND OBJECTIVE: With the rapid development of information dissemination technology, the amount of events information contained in massive texts now far exceeds the intuitive cognition of humans, and it is hard to understand the progress of events in order of time. Temporal information runs through the whole process of beginning, proceeding, and ending of events, and plays an important role in many natural language processing applications, such as information extraction, question answering, and text summary. Accurately extracting temporal information from Chinese texts and automatically mapping the temporal expressions in natural language to the time axis are crucial to understanding the development of events and dynamic changes in them.

Authors

  • Shunli Wang
    Department of Radiology, Mayo Clinic, Rochester, Minnesota, United States of America.
  • Rui Li
    Department of Oncology, Xiyuan Hospital, China Academy of Chinese Medical Science, Beijing, China.
  • Huayi Wu
    State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China.