This paper presents a new approach to improving relation extraction based on minimally supervised learning. By adding some limited closed-world knowledge for confidence estimation...
Feiyu Xu, Hans Uszkoreit, Sebastian Krause, Hong L...
The amount of information in medical publications continues to increase at a tremendous rate. Systematic reviews help to process this growing body of information. They are fundame...
We study a novel shallow information extraction problem that involves extracting sentences of a given set of topic categories from medical forum data. Given a corpus of medical fo...
In this paper, we propose a review selection approach towards accurate estimation of feature ratings for services on participatory websites where users write textual reviews for t...
We present a probabilistic generative model for learning semantic parsers from ambiguous supervision. Our approach learns from natural language sentences paired with world states ...