scholarly journals Construction of Relation Knowledge Base from Natural Language Text

2017 ◽  
Vol 29 (2) ◽  
pp. 55-64
Author(s):  
Sho TAKASE ◽  
Naoaki OKAZAKI
Keyword(s):  
Natural Language ◽  
Knowledge Base ◽  
Natural Language Text ◽  
Language Text

Automatic Extraction of Function Knowledge From Text

2015 ◽  
Author(s):  
Hyunmin Cheong ◽  
Wei Li ◽  
Adrian Cheung ◽  
Andy Nogueira ◽  
Francesco Iorio
Keyword(s):  
Natural Language ◽  
Knowledge Base ◽  
Energy Flow ◽  
Automatic Extraction ◽  
Natural Language Text ◽  
Functional Basis ◽  
Language Text

This paper presents a method to automatically extract function knowledge from natural language text. Our method uses syntactic rules to extract subject-verb-object triplets from parsed text. We then leverage the Functional Basis taxonomy, WordNet, and word2vec to classify the triplets as artifact-function-energy flow knowledge. For evaluation, we compare the function definitions associated with 30 most frequent artifacts compiled in a human-constructed knowledge base, Oregon State University’s Design Repository (DR), to those extracted using our method from 4953 Wikipedia pages classified under the category “Machines”. Our method found function definitions for 66% of the test artifacts. For those artifacts found, our method identified 50% of the function definitions compiled in DR. In addition, 75% of the most frequent function definitions found by our method were also defined in DR. The results demonstrate the promising potential of our method in automatic extraction of function knowledge.

Automated Extraction of Function Knowledge From Text

2017 ◽  
Vol 139 (11) ◽  
Author(s):  
Hyunmin Cheong ◽  
Wei Li ◽  
Adrian Cheung ◽  
Andy Nogueira ◽  
Francesco Iorio
Keyword(s):  
Natural Language ◽  
Knowledge Base ◽  
Energy Flow ◽  
Extraction Method ◽  
Current Work ◽  
Automated Extraction ◽  
Natural Language Text ◽  
Functional Basis ◽  
Knowledge Repositories ◽  
Language Text

This paper presents a method to automatically extract function knowledge from natural language text. The extraction method uses syntactic rules to acquire subject-verb-object (SVO) triplets from parsed text. Then, the functional basis taxonomy, WordNet, and word2vec are utilized to classify the triplets as artifact-function-energy flow knowledge. For evaluation, the function definitions associated with 30 most frequent artifacts compiled in a human-constructed knowledge base, Oregon State University's design repository (DR), were compared to the definitions identified by extraction the method from 4953 Wikipedia pages classified under the category “Machines.” The method found function definitions for 66% of the test artifacts. For those artifacts found, 50% of the function definitions identified were compiled in the DR. In addition, 75% of the most frequent function definitions found by the method were also defined in the DR. The results demonstrate the potential of the current work in enabling automated construction of function knowledge repositories.

Morality Classification in Natural Language Text

2020 ◽  
pp. 1-1
Author(s):  
Matheus C. Pavan ◽  
Vitor G. Santos ◽  
Alex G. J. Lan ◽  
Joao Martins ◽  
Wesley Ramos Santos ◽  
...  
Keyword(s):  
Natural Language ◽  
Natural Language Text ◽  
Language Text

scholarly journals Word-based self-indexes for natural language text

2012 ◽  
Vol 30 (1) ◽  
pp. 1-34 ◽  
Author(s):  
Antonio Fariña ◽  
Nieves R. Brisaboa ◽  
Gonzalo Navarro ◽  
Francisco Claude ◽  
Ángeles S. Places ◽  
...  
Keyword(s):  
Natural Language ◽  
Natural Language Text ◽  
Language Text
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