DocumentCode :
2772619
Title :
Natural language neural network and its application to question-answering system
Author :
Sagara, Tsukasa ; Hagiwara, Masafumi
Author_Institution :
Grad. Sch. of Sci. & Technol., Keio Univ., Yokohama, Japan
fYear :
2012
fDate :
10-15 June 2012
Firstpage :
1
Lastpage :
7
Abstract :
This paper proposes a novel neural network to treat natural language. Most of the conventional neural networks can only process sentences consisted of a few words, and their applications are very simple such as metaphor understanding. The proposed network can process many complicated sentences and can be used as an associative memory and a question-answering system. The proposed network is composed of 3 layers and one network: Sentence Layer, Knowledge Layer, Deep Case Layer and Dictionary Network. The input sentences are divided into knowledge units and stored in the Knowledge Layer. The Deep Case Layer play an important role to process the knowledge units properly. The Dictionary Network also plays an important role as a knowledge based. We have carried out several experiments and they have shown that the proposed neural network has superior performances as an associative memory and a question-answering system. Especially as a question-answering system, the performance is very close to the elaborated system based on artificial intelligence.
Keywords :
content-addressable storage; knowledge based systems; natural language processing; neural nets; question answering (information retrieval); artificial intelligence; associative memory; deep case layer; dictionary network; knowledge based system; knowledge layer; knowledge units; metaphor understanding; natural language neural network; question-answering system; sentence layer; Algorithm design and analysis; Dictionaries; Programmable logic arrays; Reliability;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks (IJCNN), The 2012 International Joint Conference on
Conference_Location :
Brisbane, QLD
ISSN :
2161-4393
Print_ISBN :
978-1-4673-1488-6
Electronic_ISBN :
2161-4393
Type :
conf
DOI :
10.1109/IJCNN.2012.6252553
Filename :
6252553
Link To Document :
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