DocumentCode :
2486068
Title :
Word Segmentation Using Domain Knowledge Based on Conditional Random Fields
Author :
Fukuda, Takuya ; Izumi, Masataka ; Miura, Takao
Author_Institution :
Hosei Univ., Tokyo
Volume :
2
fYear :
2007
fDate :
29-31 Oct. 2007
Firstpage :
436
Lastpage :
439
Abstract :
In this investigation, we propose an experimental approach for word segmentation in Japanese under domain-dependent situation. We apply Conditional Random Fields (CRF) to our issue. CRF learns several probabilistic parameters from training data with specific feature functions dependent on domains. Here we propose how to define domain specific feature functions.
Keywords :
learning (artificial intelligence); natural language processing; probability; random processes; text analysis; Japanese word segmentation; conditional random field; domain knowledge; domain specific feature function; probabilistic parameter; text processing; training data; Artificial intelligence; Dictionaries; Natural languages; Pattern analysis; Speech; Statistics; Stochastic processes; Training data;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Tools with Artificial Intelligence, 2007. ICTAI 2007. 19th IEEE International Conference on
Conference_Location :
Patras
ISSN :
1082-3409
Print_ISBN :
978-0-7695-3015-4
Type :
conf
DOI :
10.1109/ICTAI.2007.93
Filename :
4410418
Link To Document :
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