Token Identification Using HMM and PPM Models
نویسندگان
چکیده
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 domainand 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 for TCC, and for BIB. Although the performance is not as good as that obtained from a system with language-dependent components, our proposed system has power to deal with large scope of domainand language-independent problem. Identification of date has the best result, and of correct tokens are identified for two corpora respectively. The system also performs reasonably well on people’s name with correct tokens of for TCC, and for BIB.
منابع مشابه
Text Mining Using HMM and PPM
Text mining involves the use of statistical and machine learning techniques to learn structural elements of text in order to search for useful information in previously unseen text. The need for these techniques have emerged out of the rapidly growing information era. Token identification is an important component of any text mining tool. The accomplishment of this task enhances the function of...
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