Kathryn Mazaitis

TL;DR: The Never-Ending Language Learner (NELL) as discussed by the authors is a case study of a machine learning system that learns to read the Web 24hrs/day since January 2010, and so far has acquired a knowledge base with 120mn diverse, confidence-weighted beliefs (e.g., servedWith(tea,biscuits), while learning thousands of interrelated functions that continually improve its reading competence over time.

TL;DR: The Never-Ending Language Learner (NELL) as discussed by the authors is a machine learning system that learns to read the web 24 hours/day since January 2010, and so far has acquired a knowledge base with over 80 million confidence-weighted beliefs (e.g., servedWith(tea, biscuits), while continuously improving its reading competence over time.

TL;DR: GRAFT-Net as mentioned in this paper uses graph representation learning to extract answers from a question-specific subgraph containing text and knowledge base entities and relations, which is appropriate when an incomplete knowledge base is available with a large text corpus.

TL;DR: Two new large-scale datasets aimed at evaluating systems designed to comprehend a natural language query and extract its answer from a large corpus of text are presented.

TL;DR: A novel model is proposed, GRAFT-Net, for extracting answers from a question-specific subgraph containing text and Knowledge Bases entities and relations that is competitive with the state-of-the-art when tested using either KBs or text alone, and vastly outperforms existing methods in the combined setting.