Using English Dictionaries to generate Commonsense Knowledge in Natural Language

20795

Authors

nan

Abstract

AbstractThis paper presents an approach to generating common sense knowledge written in raw English sentences. Instead of using public contributors to feed this source, this system chose to employ expert linguistics decisions by using definitions from English dictionaries. Because the definitions in English dictionaries are not prepared to be transformed into inference rules, some preprocessing steps were taken to turn each relation of word:definition in dictionaries into an inference rule in the form left-hand side ⇒ right-hand side. In this paper, we applied this mechanism using two dictionaries: The MacMillan Dictionary and WordNet definitions. A random set of 200 inference rules were extracted equally from the two dictionaries, and then we used human judgment as to whether these rules are ‘True’ or not. For the MacMillan Dictionary the precision reaches 0.74 with 0.508 recall, and the WordNet definitions resulted in 0.73 precision with 0.09 recall.

Keywords

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Full Content

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Labeled Text

AbstractThis paper presents an approach to generating common sense knowledge written in raw English sentences. Instead of using public contributors to feed this source, this system chose to employ expert linguistics decisions by using definitions from English dictionaries. Because the definitions in English dictionaries are not prepared to be transformed into inference rules, some preprocessing steps were taken to turn each relation of word:definition in dictionaries into an inference rule in the form left-hand side ⇒ right-hand side. In this paper, we applied this mechanism using two dictionaries: The MacMillan Dictionary and WordNet definitions. A random set of 200 inference rules were extracted equally from the two dictionaries, and then we used human judgment as to whether these rules are ‘True’ or not. For the MacMillan Dictionary the precision reaches 0.74 with 0.508 recall, and the WordNet definitions resulted in 0.73 precision with 0.09 recall.

Publication Details

Paper ID:
20795
Year:
2017
Publication:
TIPSTER
Publication Type:
conf
DOI:
nan