A Neural Network for Factoid Question Answering over Paragraphs

@inproceedings{Iyyer2014ANN,
  title={A Neural Network for Factoid Question Answering over Paragraphs},
  author={Mohit Iyyer and Jordan L. Boyd-Graber and Leonardo Claudino and R. Socher and Hal Daum{\'e}},
  booktitle={EMNLP},
  year={2014}
}
  • Mohit Iyyer, Jordan L. Boyd-Graber, +2 authors Hal Daumé
  • Published in EMNLP 2014
  • Computer Science
  • Text classification methods for tasks like factoid question answering typically use manually defined string matching rules or bag of words representations. These methods are ineective when question text contains very few individual words (e.g., named entities) that are indicative of the answer. We introduce a recursive neural network (rnn) model that can reason over such input by modeling textual compositionality. We apply our model, qanta, to a dataset of questions from a trivia competition… CONTINUE READING
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