A New Method to Build NLP Knowledge for Improving Term Disambiguation

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.

@article{Abdelrahim2016,
  title     = {A New Method to Build NLP Knowledge for Improving Term Disambiguation},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {7},
  number    = {7},
  year      = {2016},
  publisher = {The Science and Information Organization},
  author    = {E. MD. Abdelrahim and El-Sayed Atlam and R. F. Mansour},
  doi       = {10.14569/IJACSA.2016.070704},
  url       = {https://doi.org/10.14569/IJACSA.2016.070704}
}