The And Temporal Information Extraction

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Much of the information extraction community early focus on in tasks like named entity recognition, co-reference and relation extraction, but recently due to the high demand of temporal information in different NLP application, scholars are focusing on event, temporal and event and temporal information extraction and the extraction of event and temporal information became a hot research area. Different scholars involved in event and temporal information extraction researches. Currently there are a lot of research conducted in event and temporal information extraction in different domains and languages with different techniques, methods and tools. In this chapter, we present some of the works conducted in the English language related to this thesis project. The first work we discuss is called TIE, Temporal Information Extraction system extracts events from text by inducing as much as temporal information possible. TIE makes global inference, enforcing transitivity to bound the beginning and finishing time for each event. TIE introduces temporal entropy as a method to evaluate the performance of temporal IE system. TIE system outperforms in experiment in three optional approaches. The TIE system uses a probabilistic method to recognize temporal rations. They use TimeBank data [25] to train the system. TIE processes, each natural language sentence into two sequential phases. The first phase is responsible for extracting event and identifying temporal expression and it use a
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