Leveraging Unlabeled Data for Entity-Relation Extraction through Probabilistic Constraint Satisfaction
Year: 2021
Type: preprint
Abstract: We study the problem of entity-relation extraction in the presence of symbolic domain knowledge. Such knowledge takes the form of an ontology defining relations and their permissible arguments. Previous approaches set out to integrate such knowledge in their learning approaches either through self-training, or through approximations that lose the precise meaning of the logical expressions. By contrast, our approach employs semantic loss which captures the precise meaning of a logical sentence through maintaining a probability distribution over all possible states, and guiding the model to solutions which minimize any constraint violations. With a focus on low-data regimes, we show that semantic loss outperforms the baselines by a wide margin.
Source: arXiv (Cornell University)
Authors: Ahmed, Kareem, Wang, Eric, Broeck, Guy Van den, Chang, Kai-Wei
Institution: University of California, Los Angeles
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3137084394