The genesis of Extractor was a thesis exploring the application of artificial intelligence and machine learning. Specifically, how the growing proliferation of information and intellectual property, primarily via the World Wide Web, could be refined and sourced with certainty and relevance. The application of artificial intelligence married with the theories of machine learning would prove effective. 

We know and experience today the great results of Dr. Turney's research efforts, now in version 7.2, Extractor is a proven solution providing developers with the tool for better sourcing of information and most importantly its contextual meaning. The scientific research that went into the creation of the Extractor Technology is found in the following published documentation:
 
          Turney, P.D. (2000).
Learning algorithms for keyphrase extraction.
Information Retrieval, 2 (4): 303-336.
 
          Mathieu, J. (1999).
Adaptation of a keyphrase extractor for Japanese text.
Proceedings of the 27th Annual Conference of the Canadian Association for Information Science (CAIS-99),
Sherbrooke, Quebec, pp. 182-189.
 
          Turney, P.D. (1999).
Learning to Extract Keyphrases from Text.
NRC Technical Report ERB-1057, National Research Council Canada.
 
          Turney, P.D. (1997).
Extraction of Keyphrases from Text: Evaluation of Four Algorithms.
NRC Technical Report ERB-1051, National Research Council Canada.
 

          Answering Subcognitive Turing Test Questions: A Reply to French

          Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL

 
    


    
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