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Domain Independent Keyword Identification for Question AnsweringAuthors: Prathyusha Jwalapuram,Radhika Mamidi Conference: 21st International Conference on Asian Language Processing (IALP-2017 2017) Location Singapore Date: 2017-12-05 Report no: IIIT/TR/2017/89 AbstractIn this paper, we look at domain independent keyword identification for natural language queries using statistical methods. We took queries supplemented by only their dependency tags (Stanford Parser) and part-of-speech tags (Stanford POS tagger) and labeled the keywords. We then delexicalised the training data, and used the Conditional Random Fields algorithm to learn these labels. We used the queries created by [1] in the course management domain for training, and tested our model on the queries of three domains: course management, library and the G EO Q UERIES 250 dataset and report fairly high accuracies of 90.65%, 83.19% and 97.13% respectively, making our model a truly domain independent and highly accurate keyword identifier. Full paper: pdf Centre for Language Technologies Research Centre |
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