Abstract
Term sense disambiguation is very essential for different approaches of NLP, including Internet search engines, information retrieval, Data mining, classification etc. However, the old methods using case frames and semantic primitives are not qualify for solving term ambiguities which needs a lot of information with sentences. This new approach introduces a building structure system of natural language knowledge. In this paper all surface case patterns is classified in advance with the consideration of the meaning of noun. Moreover, this paper introduces an efficient data structure using a trie which define the linkage among leaves and multi-attribute relations. By using this linkage multi-attribute relations, we can get a high frequent access among verbs and noun with an automatic generation of hierarchical relationships. In our experiment a large tagged corpus (Pan Treebank) is used to extract data. In our approach around 11,000 verbs and nouns is used for verifying the new method and made a hierarchy group of its noun. Moreover, the achievement of term disambiguating using our trie structure method and linking trie among leaves is 6% higher than old method.
Highlights
Natural language processing (NLP) systems use many dictionaries
As follow: The first approach: semantic meaning of bank is financial house in the first sentence, this by using another verbs to declare this meaning as in these sentence say : Jhon exchange from bank. and for more information about bank we can say that : Bank buy money. By this more information we find all sentence speak about money this implies more disambiguate for word bank and the clear semantic is financial institution .the second approach: semantic meaning of bank is edge of river in the second sentence, this by using another verb to declare this meaning as in these sentence: Sandbags maintain bank, and for more information about bank we can say: Bank maintain river i.e
In this paper, we proposed a new approach for building structure system of natural language knowledge
Summary
1,3 Computer science department, Faculty of science, Northern Border University, KSA) 1,2 Dept. of Mathematics, Computer Science Division, Faculty of Science, Tanta, Egypt 2Dept. of Information Science and Intelligent Systems, University of Tokushima, Tokushima, Japan 770-8506. The old methods using case frames and semantic primitives are not qualify for solving term ambiguities which needs a lot of information with sentences. This new approach introduces a building structure system of natural language knowledge. This paper introduces an efficient data structure using a trie which define the linkage among leaves and multi-attribute relations. By using this linkage multi-attribute relations, we can get a high frequent access among verbs and noun with an automatic generation of hierarchical relationships.
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