This paper proposes a unified Transformation Based Learning (TBL, Brill, 1995) framework for Chinese Entity Detection and Tracking (EDT). It consists of two sub models: a mention detection model and an entity tracking/coreference model. The first sub-model is used to adapt existing Chinese word segmentation and Named Entity (NE) recognition results to a specific EDT standard to find all the mentions. The second sub-model is used to find the coreference relation between the mentions. In addition, a feedback technique is proposed to further improve the performance of the system. We evaluated our methods on the Automatic Content Extraction (ACE, NIST, 2003) Chinese EDT corpus. Results show that it outperforms the baseline, and achieves comparable performance with the stateof-the-art methods.
Finally, REF proposed a unified Transformation-Based Learning framework on Chinese EDT.
Transformation Based Chinese Entity Detection and Tracking
Second International Joint Conference on Natural Language Processing: Companion Volume including Posters/Demos and tutorial abstracts
Computer Science