Token Identification Using HMM and PPM Models

Hidden markov models (HMMs) and prediction by partial matching models (PPM) have been successfully used in language processing tasks including learning-based token identification. Most of the existing systems are domainand language-dependent. The power of retargetability and applicability of these systems is limited. This paper investigates the effect of the combination of HMMs and PPM on token identification. We implement a system that bridges the two well known methods through words new to the identification model. The system is fully domain- and language-independent. No changes of code are necessary when applying to other domains or languages. The only required input of the system is an annotated corpus. The system has been tested on two corpora and achieved an overall F-measure of

• Type: Conference
• Year: 2003
• Where: AUSAI
• Authors: Yingying Wen, Ian H. Witten, Dianhui Wang
• Keywords: Artificial Intelligence, AUSAI 2003, Hidden Markov Models, Token Identification, Token Identification Task