Comparative study of machine learning techniques for boundary determination of explanation knowledge from text

Autor:	Chaveevan Pechsiri, Patrick Saint-Dizier, Rapeepun Piriyakul
Rok vydání:	2009
Předmět:	business.industry Computer science Principle of maximum entropy Bayesian network Machine learning computer.software_genre Naive Bayes classifier Entropy (information theory) Artificial intelligence business computer Discourse marker Natural language processing Support vector machine classification
Zdroj:	2009 Eighth International Symposium on Natural Language Processing.
DOI:	10.1109/snlp.2009.5340938
Popis:	This research aim to determine the explanation knowledge boundary for improvement of basic diagnosis. This paper compares different machine learning techniques including Maximum Entropy, Bayesian Networks, and Naive Bayes for solving the boundary determination problems of the discourse marker's connection problem, usage of several discourse markers within the boundary, and implicit discourse marker. The results have shown an improvement through using machine learning techniques comparing with Centering Theory used in the previous work.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::3b0c89316339ca73c1f87a6cbbe45501 https://doi.org/10.1109/snlp.2009.5340938 Zobrazit plný text záznamu