A Human-Machine Language Dictionary
, Shirin Akther Khanam, Yi-Ping Phoebe Chen- DOI
- 10.2991/ijcis.d.200602.002How to use a DOI?
- Keywords
- Text mining; Natural language processing; Knowledge representation
- Abstract
In this paper, we propose a framework for building a human-machine language dictionary. Given a concept/word, an application can extract the definition of the concept from the dictionary, and consequently “understand” its meaning. In the dictionary, a concept is defined through its relations with other concepts. Relations are specified in the machine language. To a certain degree, the proposed dictionary has a resemblance to WordNet, which consists of a set of concepts/words with synonyms being linked to form the net. WordNet plays an important role in text mining, such as sentiment analysis, document classification, text summarization and question answering systems, etc. However, merely providing synonyms is not sufficient. The proposed dictionary provides a definition for each concept. Based on the definition, the application can accurately estimate the distance and similarity between concepts. As a monotonic mapping, the algorithm for estimating distances and similarities is proved to be always convergent. We envisage that the dictionary will become an important tool in all Text Mining disciplines.
- Copyright
- © 2020 The Authors. Published by Atlantis Press SARL.
- Open Access
- This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).
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TY - JOUR AU - Fei Liu AU - Shirin Akther Khanam AU - Yi-Ping Phoebe Chen PY - 2020 DA - 2020/06/18 TI - A Human-Machine Language Dictionary JO - International Journal of Computational Intelligence Systems SP - 904 EP - 913 VL - 13 IS - 1 SN - 1875-6883 UR - https://doi.org/10.2991/ijcis.d.200602.002 DO - 10.2991/ijcis.d.200602.002 ID - Liu2020 ER -