This paper presents a method for improving the performance of syntactic analysis by using accurate temporal expression processing. Temporal expression causes parsing errors due to its syntactic duality, but its resolution is not trivial since the syntactic role of temporal expression is understandable in the context. In our work, syntactic functions of temporal words are decisively identified based on local contexts of individual temporal words acquired from a large corpus, which are represented by a finite state method. Experimental results show how the proposed method, incorporated with parsing, improves the accuracy and efficiency of the syntactic analysis.

In this paper, we describe a two-phase method for biomedical named entity recognition consisting of term boundary detection and biomedical category labeling. The term boundary detection can be defined as a task to assign label sequences to a given sentence, and biomedical category labeling can be viewed as a local classification problem which does not need knowledge of the labels of other named entities in a sentence. The advantage of dividing the recognition process into two phases is that we can measure the effectiveness of models at each phase and select separately the appropriate model for each subtask. In order to obtain a better performance in biomedical named entity recognition, we conducted comparative experiments using several learning methods at each phase. Moreover, results by these machine learning based models are refined by rule-based postprocessing. We tested our methods on the JNLPBA 2004 shared task and the GENIA corpus.