15,229 research outputs found
A Hierarchical Framework for Relation Extraction with Reinforcement Learning
Most existing methods determine relation types only after all the entities
have been recognized, thus the interaction between relation types and entity
mentions is not fully modeled. This paper presents a novel paradigm to deal
with relation extraction by regarding the related entities as the arguments of
a relation. We apply a hierarchical reinforcement learning (HRL) framework in
this paradigm to enhance the interaction between entity mentions and relation
types. The whole extraction process is decomposed into a hierarchy of two-level
RL policies for relation detection and entity extraction respectively, so that
it is more feasible and natural to deal with overlapping relations. Our model
was evaluated on public datasets collected via distant supervision, and results
show that it gains better performance than existing methods and is more
powerful for extracting overlapping relations.Comment: To appear in AAAI 1
Improving Information Extraction by Acquiring External Evidence with Reinforcement Learning
Most successful information extraction systems operate with access to a large
collection of documents. In this work, we explore the task of acquiring and
incorporating external evidence to improve extraction accuracy in domains where
the amount of training data is scarce. This process entails issuing search
queries, extraction from new sources and reconciliation of extracted values,
which are repeated until sufficient evidence is collected. We approach the
problem using a reinforcement learning framework where our model learns to
select optimal actions based on contextual information. We employ a deep
Q-network, trained to optimize a reward function that reflects extraction
accuracy while penalizing extra effort. Our experiments on two databases -- of
shooting incidents, and food adulteration cases -- demonstrate that our system
significantly outperforms traditional extractors and a competitive
meta-classifier baseline.Comment: Appearing in EMNLP 2016 (12 pages incl. supplementary material
- …