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Application and Difficulty of Natural Language Processing in Chinese Temporal Information Extraction

The conventional information system cannot cater for temporal information effectively. It may not be able to handle users’ queries concerning how an action relates to another in time. It is useful to capture and maintain the temporal knowledge associated to each action in an information system. In this paper, we proposed a general frame structure for maintaining the extracted temporal concepts and proposed a system for extracting time-dependent information from Hong Kong financial news, known as TICS. In the system, temporal knowledge is represented in the forms of types of temporal concepts and temporal relations. When analyzing a sentence, TICS first determines the situation related to the predicate verb. This in turn will identify the type of temporal concept. Based on that, the relevant temporal information is extracted and temporal relations are derived. These relations link relevant concept frames together in chronological order, and provide the knowledge to fulfill users' queries.