Sencept: a Domain-Specific Textual Commonsense Concept Acquisition System
International Journal of Computer and Information Technology (ISSN: 2279 – 0764) Volume 01– Issue 02, November 2012
SenCept: A Domain-specific Textual Commonsense Concept Acquisition System
Rushdi Shams1, M.S.A. Shahnawaz Chowdhury2, and S.M. Abu Saleh Shawon3
Computational Linguistics Lab Department of Computer Science University of Western Ontario, London, Ontario, N6A 5B7, Canada1 Department of Computer Science and Engineering Khulna University of Engineering & Technology (KUET) Khulna-9203, Bangladesh2,3 1 rshams@csd.uwo.ca , sajib014@gmail.com2, shawon16@gmail.com3
Abstract— In this paper, we report the development and the performance of SenCept that acquires textual commonsense concepts to offer better contextualization for the domain DC electrical circuits. It uses a commonsense knowledge-base built upon a linguistic relations framework comprising Clause Level Relations, Sentential Roles, and Rhetorical Relations of a domain-specific corpus. SenCept selects representative commonsense knowledge using several parameters like knowledge weight and average commonsensical distances among knowledge. To extract commonsense concepts for any given sentence, the system uses the latter and the mean of distances among normalized weights of the representative sentences. The system is tested with a set of 100 random domain-specific sentences that are also given to five human subjects. Results show that SenCept achieves a precision and recall of 71.43 and 51.77 percent, respectively with an F-score of 60.03 percent. Keywords- Commonsense knowledge, commonsense concept, knowledge acquisition, knowledge engineering.
commonsense but does not have the knowledge. Finally, in his youth, he has both knowledge and commonsense. Although they have subtle differences between them, commonsense is a type of knowledge. Knowledge varies in human but commonsense should not and it should be present commonly in us- it is what makes the identification of exact commonsense a
SenCept: A Domain-specific Textual Commonsense Concept Acquisition System
Rushdi Shams1, M.S.A. Shahnawaz Chowdhury2, and S.M. Abu Saleh Shawon3
Computational Linguistics Lab Department of Computer Science University of Western Ontario, London, Ontario, N6A 5B7, Canada1 Department of Computer Science and Engineering Khulna University of Engineering & Technology (KUET) Khulna-9203, Bangladesh2,3 1 rshams@csd.uwo.ca , sajib014@gmail.com2, shawon16@gmail.com3
Abstract— In this paper, we report the development and the performance of SenCept that acquires textual commonsense concepts to offer better contextualization for the domain DC electrical circuits. It uses a commonsense knowledge-base built upon a linguistic relations framework comprising Clause Level Relations, Sentential Roles, and Rhetorical Relations of a domain-specific corpus. SenCept selects representative commonsense knowledge using several parameters like knowledge weight and average commonsensical distances among knowledge. To extract commonsense concepts for any given sentence, the system uses the latter and the mean of distances among normalized weights of the representative sentences. The system is tested with a set of 100 random domain-specific sentences that are also given to five human subjects. Results show that SenCept achieves a precision and recall of 71.43 and 51.77 percent, respectively with an F-score of 60.03 percent. Keywords- Commonsense knowledge, commonsense concept, knowledge acquisition, knowledge engineering.
commonsense but does not have the knowledge. Finally, in his youth, he has both knowledge and commonsense. Although they have subtle differences between them, commonsense is a type of knowledge. Knowledge varies in human but commonsense should not and it should be present commonly in us- it is what makes the identification of exact commonsense a
References: International Journal of Computer and Information Technology (ISSN: 2279 – 0764) Volume 01– Issue 02, November 2012 of A1 is not necessary International Journal of Computer and Information Technology (ISSN: 2279 – 0764) Volume 01– Issue 02, November 2012 Topic Shifter: The writer explicitly changes the topic