Learning to Answer Questions from Wikipedia Infoboxes
A natural language interface to answers on the Web can help us access information more efficiently. We start with an interesting source of information infoboxes in Wikipedia that summarize factoid knowledge and develop a comprehensive approach to answering questions with high precision. We first build a system to access data in infoboxes in a structured manner. We use our system to construct a crowdsourced dataset of over 15,000 highquality, diverse questions. With these questions, we train a convolutional neural network model that outperforms models that achieve top results in similar answer selection tasks.
EMNLP