Improvement for the automatic part-of-speech tagging based on hidden Markov model

Yuan, Lichi

doi:10.1109/icsps.2010.5555259

Cited by 7 publications

(5 citation statements)

References 4 publications

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“…Static features gives candidate phrases the possibilities of constituting certain structural type of verb phrases, and whether it can really constitute verb phrase in the sentence depends on the context [11,12]. Thus, dynamic characters can be divided in two types-environmental features on grammatically partial words and environmental features on candidates.…”

Section: Dynamic Feature Extractionmentioning

confidence: 99%

“…SVM is a small sample learning method based on statistical learning theory in the mid 1990 by Vapnik et al in Bell Lab, with a rigorous theoretical basis [4][5][6][7]11]. Based on structural risk minimization criteria, it has stronger generalization ability and can better solve the practice problems such as the small sample, non-linear, highly practical problems and the local minimum dimension, thus it becomes one of the fastest research directions of the recent developments in the field of machine learning.…”

Section: Introductionmentioning

confidence: 99%

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A Classification Algorithm for Chinese Verb Phrases Using Support Vector Machine

Cao¹,

Wang²

2014

TOAUTOCJ

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Chinese verb phrases classification is to determine boundaries of verb phrases and divide them exactly, using brackets, by automatically analyzing and processing by computer after the sentences have been decollated and marked the characteristic or property of a certain word. SVM classification model is a common and powerful for classification tasks. In this paper, the SVM classification model is built by extracting static features and dynamic features of Chinese verb phrases, and an algorithm to perform Chinese verb phrases classification using support vector machine is proposed. Using 3500 sentences to train and test, experiment results show that the SVM model dramatically reduces the training time and steps. Compared with the method proposed in literature 15, classification precision rate is increased by approximately 8.0% using the algorithm in this paper, which fully illustrates that the performance of the proposed algorithm is superior classification algorithm.

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Section: Dynamic Feature Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Classification Algorithm for Chinese Verb Phrases Using Support Vector Machine

Cao¹,

Wang²

2014

TOAUTOCJ

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“…If the lexical class of the word is known, then performing linguistic analysis becomes much easier (Yoshida et al, 2007). Various approaches are mentioned in the scientific literature for implementing POS tagging based on dictionaries (Yuan, 2010). The most promising approaches used are rule-based MA and stochastic model, such as Hidden Markov Model (HMM).…”

Section: Feature Extractionmentioning

confidence: 99%

“…In a rule-based approach, the text is decomposed into tokens that can be further used for analysis. Moreover, HMM is a stochastic tagging technique mainly used to discover the most similar POS tagging from sequence of input tokens (Yuan, 2010). Parsing is a technique used for examining the grammatical structure of a sentence.…”

Section: Feature Extractionmentioning

confidence: 99%

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A survey on text mining in social networks

Irfan¹,

King²,

Grages³

et al. 2015

The Knowledge Engineering Review

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In this survey, we review different text mining techniques to discover various textual patterns from the social networking sites. Social network applications create opportunities to establish interaction among people leading to mutual learning and sharing of valuable knowledge, such as chat, comments, and discussion boards. Data in social networking websites is inherently unstructured and fuzzy in nature. In everyday life conversations, people do not care about the spellings and accurate grammatical construction of a sentence that may lead to different types of ambiguities, such as lexical, syntactic, and semantic. Therefore, analyzing and extracting information patterns from such data sets are more complex. Several surveys have been conducted to analyze different methods for the information extraction. Most of the surveys emphasized on the application of different text mining techniques for unstructured data sets reside in the form of text documents, but do not specifically target the data sets in social networking website. This survey attempts to provide a thorough understanding of different text mining techniques as well as the application of these techniques in the social networking websites. This survey investigates the recent advancement in the field of text analysis and covers two basic approaches of text mining, such as classification and clustering that are widely used for the exploration of the unstructured text available on the Web.

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Text Mining and Real-Time Analytics of Twitter Data: A Case Study of Australian Hay Fever Prediction

Subramani

Michalska

Wang

et al. 2018

Health Information Science

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Improvement for the automatic part-of-speech tagging based on hidden Markov model

Cited by 7 publications

References 4 publications

A Classification Algorithm for Chinese Verb Phrases Using Support Vector Machine

A Classification Algorithm for Chinese Verb Phrases Using Support Vector Machine

A survey on text mining in social networks

Text Mining and Real-Time Analytics of Twitter Data: A Case Study of Australian Hay Fever Prediction

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