Chang Wang

Relation extraction and scoring in DeepQA

Detecting semantic relations in text is an active problem area in natural-language processing and information retrieval. For question answering, there are many advantages of detecting relations in the question text because it allows background relational knowledge to be used to generate potential answers or find additional evidence to score supporting passages. This paper presents two approaches to broad-domain relation extraction and scoring in the DeepQA question-answering framework, i.e., one based on manual pattern specification and the other relying on statistical methods for pattern elicitation, which uses a novel transfer learning technique, i.e., relation topics. These two approaches are complementary; the rule-based approach is more precise and is used by several DeepQA components, but it requires manual effort, which allows for coverage on only a small targeted set of relations (approximately 30). Statistical approaches, on the other hand, automatically learn how to extract semantic relations from the training data and can be applied to detect a large amount of relations (approximately 7,000). Although the precision of the statistical relation detectors is not as high as that of the rule-based approach, their overall impact on the system through passage scoring is statistically significant because of their broad coverage of knowledge.

Medical Relation Extraction with Manifold Models

In this paper, we present a manifold model for medical relation extraction. Our model is built upon a medical corpus containing 80M sentences (11 gigabyte text) and designed to accurately and efciently detect the key medical relations that can facilitate clinical decision making. Our approach integrates domain specic parsing and typing systems, and can utilize labeled as well as unlabeled examples. To provide users with more e xibility, we also take label weight into consideration. Effectiveness of our model is demonstrated both theoretically with a proof to show that the solution is a closed-form solution and experimentally with positive results in experiments.