  Accurately typing entity mentions from text segments is a fundamental task
for various natural language processing applications. Many previous approaches
rely on massive human-annotated data to perform entity typing. Nevertheless,
collecting such data in highly specialized science and engineering domains
(e.g., software engineering and security) can be time-consuming and costly,
without mentioning the domain gaps between training and inference data if the
model needs to be applied to confidential datasets. In this paper, we study the
task of seed-guided fine-grained entity typing in science and engineering
domains, which takes the name and a few seed entities for each entity type as
the only supervision and aims to classify new entity mentions into both seen
and unseen types (i.e., those without seed entities). To solve this problem, we
propose SEType which first enriches the weak supervision by finding more
entities for each seen type from an unlabeled corpus using the contextualized
representations of pre-trained language models. It then matches the enriched
entities to unlabeled text to get pseudo-labeled samples and trains a textual
entailment model that can make inferences for both seen and unseen types.
Extensive experiments on two datasets covering four domains demonstrate the
effectiveness of SEType in comparison with various baselines.
