A conditional random field framework for language process in product review mining

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Detalles Bibliográficos
Publicado en:	Multimedia Tools and Applications vol. 82, no. 1 (Jan 2023), p. 803
Autor principal:	Ming, Yue
Otros Autores:	Liu, Xiyuan, Shen, Gang, Gao, Di, Wang, Yu
Publicado:	Springer Nature B.V.
Materias:	Feature extraction Speech Datasets Algorithms Data mining Markov chains Natural language processing Product reviews Computing time Marking Conditional random fields Sentiment analysis
Acceso en línea:	Citation/Abstract Full Text - PDF
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Descripción
Resumen:	The Part-Of-Speech tagging is widely used in the natural language process. There are many statistical approaches in this area. The most popular one is Hidden Markov Model. In this paper, an alternative approach, linear-chain Conditional Random Fields, is introduced. The Conditional Random Fields is a factor graph approach that can naturally incorporate arbitrary, non-independent features of the input without conditional independence among the features or distributional assumptions of inputs. This paper applied the Conditional Random Fields for the car review word Part-Of-Speech tagging and then the feature extraction, which can be used as an input to an opinion mining system. To reduce the computational time, we also proposed applying the Limited-memory BFGS algorithm to train the Conditional Random Fields. Furthermore, this paper evaluated the Conditional Random Fields and the classical graph approach using the car review dataset to demonstrate that the Conditional Random Fields have a more robust result with a smaller training dataset.
ISSN:	1380-7501 1573-7721
DOI:	10.1007/s11042-022-13303-2
Fuente:	ABI/INFORM Global