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Commonsense knowledge Extraction for Persian Language:A Combinatory Approach

Moradi, Mehdi | 2013

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  1. Type of Document: M.Sc. Thesis
  2. Language: English
  3. Document No: 44144 (31)
  4. University: Sharif University of Technology
  5. Department: Languages and Linguistics Center
  6. Advisor(s): Vazirnezhad, Bahram; Bahrani, Mohammad
  7. Abstract:
  8. Putting human commonsense knowledge into computers has always been a long standing dream of artificial intelligence (AI). Since the first days of its appearance, AI knowledge engineers were studying hard to get round this bottleneck. The cost of several tens of millions of dollars and times have been covered so that the computers could know about “objects falling, not rising.”,” running is faster than walking" And “death is the end of the life”. The large database was built, automated and semi-automated methods were introduced and volunteers’ efforts were utilized to achieve this, but an automated, high-throughput and low-noise method for commonsense collection still remains as the holy grail of AI.
    The aim of this study was to extract commonsense knowledge using three approaches namely Hearst method, machine translation and using structured resources and finally sentiment analysis of user’s comments by means of this knowledge. Applying aforementioned methods, we could extract 7 different relation including IsA, MadeOf, UsedFor, Part of, LocationOf, PropertyOf and Synonymy from Persian resources and OMC corpus. 85000 assertions have been extracted. Finally, average accuracy of Hearst, MT and structured resource were 75%, 75% and 100% respectively. The result of sentiment analysis using this relation demonstrated accuracy of 65% on the sentence level
  9. Keywords:
  10. Relation Extraction ; Common Sense ; Ontology

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