Two-stage model selection with parameters weighted hidden Markov models and likelihood ratio for part-of-speech tagging

Title:

Two-stage model selection with parameters weighted hidden Markov models and likelihood ratio for part-of-speech tagging

Creator:

Sun, Schichang, Liu, Hongbo, Zhao, Pixo , and Lin, Hongfei

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:002c5d2b-6f8c-4acf-8321-79a44dfd9c1d
uuid:002c5d2b-6f8c-4acf-8321-79a44dfd9c1d
doi:10.14311/NNW.2012.22.014

Subject:

Hidden Markov models, model selection, part-of speech tagging, and likehood ration

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

In many natural language processing applications two or more models usually have to be involved for accuracy. But it is difficult for minor models, such as “backoff” taggers in part-of-speech tagging, to cooperate smoothly with the major probabilistic model. We introduce a two-stage approach for model selection between hidden Markov models and other minor models. In the first stage, the major model is extended to give a set of candidates for model selection. Parameters weighted hidden Markov model is presented using weighted ratio to create the candidate set. In the second stage, heuristic rules and features are used as evaluation functions to give extra scores to candidates in the set. Such scores are calculated using a diagnostic likelihood ratio test based on sensitivity and specificity criteria. The selection procedure can be fulfilled using swarm optimization technique. Experiment results on public tagging data sets show the applicability of the proposed approach.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2012 Volume:22 | Number:3

Harvested from:

CDK

Metadata only:

false