|Title||Learning to Answer Questions from Wikipedia Infoboxes|
|Publication Type||Conference Paper|
|Year of Publication||2016|
|Authors||Morales, A, Premtoon, V, Avery, C, Felshin, S, Katz, B|
|Conference Name||The 2016 Conference on Empirical Methods on Natural Language Processing (EMNLP 2016)|
A natural language interface to answers on the Web can help us access information more ef- ficiently. We start with an interesting source of information—infoboxes in Wikipedia that summarize factoid knowledge—and develop a comprehensive approach to answering ques- tions with high precision. We first build a system to access data in infoboxes in a struc- tured manner. We use our system to construct a crowdsourced dataset of over 15,000 high- quality, diverse questions. With these ques- tions, we train a convolutional neural network model that outperforms models that achieve top results in similar answer selection tasks.
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