<abstract xmlns="http://www.w3.org/1999/xhtml"><p>In this paper, we present a wide range of models based on less adaptive and adaptive approaches for a PoS tagging system. These parameters for the adaptive approach are based on the n-gram of the Hidden Markov Model, evaluated for bigram and trigram, and based on three different types of decoding method, in this case forward, backward, and bidirectional. We used the Brown Corpus for the training and the testing phase. The bidirectional trigram model almost reaches state of the art accuracy but is disadvantaged by the decoding speed time while the backward trigram reaches almost the same results with a way better decoding speed time. By these results, we can conclude that the decoding procedure it’s way better when it evaluates the sentence from the last word to the first word and although the backward trigram model is very good, we still recommend the bidirectional trigram model when we want good precision on real data.</p></abstract>

In this paper, we present a wide range of models based on less adaptive and adaptive approaches for a PoS tagging system. These parameters for the adaptive approach are based on the n-gram of the Hidden Markov Model, evaluated for bigram and trigram, and based on three different types of decoding method, in this case forward, backward, and bidirectional. We used the Brown Corpus for the training and the testing phase. The bidirectional trigram model almost reaches state of the art accuracy but is disadvantaged by the decoding speed time while the backward trigram reaches almost the same results with a way better decoding speed time. By these results, we can conclude that the decoding procedure it’s way better when it evaluates the sentence from the last word to the first word and although the backward trigram model is very good, we still recommend the bidirectional trigram model when we want good precision on real data.

Part of Speech Tagging Using Hidden Markov Models

Computer Science and Electrical and Electronics Engineering Department, Faculty of Engineering

International Journal of Advanced Statistics and IT&C for Economics and Life Sciences

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Part of Speech Tagging Using Hidden Markov Models"}

In this paper, we present a wide range of models based on less adaptive and adaptive approaches for a PoS tagging system. These parameters for the adaptive...

Part of Speech Tagging Using Hidden Markov Models

Online veröffentlicht: 24. Dez. 2020

Seitenbereich: 31 - 42

DOI: https://doi.org/10.2478/ijasitels-2020-0005

Schlüsselwörter
Part of Speech, Hidden Markov Model, rule-based tagger, word structure analysis

© 2020 Adrian Bărbulescu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Part of Speech Tagging Using Hidden Markov Models

Online veröffentlicht: 24. Dez. 2020

Seitenbereich: 31 - 42

DOI: https://doi.org/10.2478/ijasitels-2020-0005

SchlüsselwörterPart of Speech, Hidden Markov Model, rule-based tagger, word structure analysis

© 2020 Adrian Bărbulescu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
Part of Speech, Hidden Markov Model, rule-based tagger, word structure analysis