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A Joint Segmenting and Labeling Approach for Chinese Lexical Analysis

A Joint Segmenting and Labeling Approach for Chinese Lexical Analysis

This video was recorded at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Antwerp 2008. This paper introduces an approach which jointly performs a cascade of segmentation and labeling subtasks for Chinese lexical analysis, including word segmentation, named entity recognition and part-of-speech tagging. Unlike the traditional pipeline manner, the cascaded subtasks are conducted in a single step simultaneously, therefore error propagation could be avoided and the information could be shared among multi-level subtasks. In this approach, Weighted Finite State Transducers (WFSTs) are adopted. Within the unified framework of WFSTs, the models for each subtask are represented and then combined into a single one. Thereby, through one-pass decoding the joint optimal outputs for multi-level processes will be reached. The experimental results show the effectiveness of the presented joint processing approach, which significantly outperforms the traditional method in pipeline style.

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