not perfectly, but much better than Rosetta stone
one that selects answers at random. Random selection got the answer (as determined by human inspection) right 87 out of 314 times, where the best implementation of the HITS system was correct 221 times. The ISI implementation of HITS integrates three separate elements--speech act analysis, lexical similarity, and poster trustworthiness--to create links for interpretation for individual conversation participants. Speech act analysis classifies the statements in the record according to what they do in the context of the discussion, assigning each to one of thirteen kinds of acts, grouped in three categories: inform, request, social-interaction. The inform speech act category includes corrections, descriptions, elaborations, suggestions, and answers to questions, both simple and complex. Requests include not just requests for information but also for action, namely commands. Social speech acts include acknowledgements, Rosetta Stone Spanish (Spain) Levev 1-5
thanks, compliments, criticisms, objections, and supportive statements. Lexical analysis looks for similarities in the vocabulary of responses to see which are related to each other. From this the system can determine the threads of the conversation, and decide when new subtopics are split off. Finally, poster trustworthiness measures the degree to which participants accept statements made by each individual. This is determined by scoring responses to a given person's posts as either negative or positive. Over time, people whose statements are more positively viewed become more central and more trusted in the online community. To test the method, part of the data (the classification of the speech acts) was initially human coded. After it was trained, the machine system was then applied to the same data, and its performance was compared to that of the human coder. It achieved accuracy of between Rosetta Stone Spanish
65% and 70% -- a figure that is likely to improve. How soon will it be possible to download a version that can score a given poster's influence in his/her chat community? This technology has considerable potential for commercialization, said Hovy.Besides Hovy, the other members of the conversation study include ISI computer scientists Erin Shaw and Jihie Kim as well as graduate student Donghui Feng. DARPA supported the research. The presentation will be at the June 5-7 Human Language Technology Conference at NYU in New York.



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