18 citations · 25 across the 5 of their papers we have counts for
5 papers
From Ultra-Fine to Fine: Fine-tuning Ultra-Fine Entity Typing Models to Fine-grained
Hongliang Dai, Ziqian Zeng
For the task of fine-grained entity typing (FET), due to the use of a large number of entity types, it is usually considered too costly to manually annotating a training dataset th…
Ultra-Fine Entity Typing with Weak Supervision from a Masked Language Model
Hongliang Dai, Yangqiu Song, Haixun Wang
Recently, there is an effort to extend fine-grained entity typing by using a richer and ultra-fine set of types, and labeling noun phrases including pronouns and nominal nouns inst…
A Chinese Corpus for Fine-grained Entity Typing
Chin Lee, Hongliang Dai, Yangqiu Song +1
Fine-grained entity typing is a challenging task with wide applications. However, most existing datasets for this task are in English. In this paper, we introduce a corpus for Chin…
Improving Fine-grained Entity Typing with Entity Linking
Hongliang Dai, Donghong Du, Xin Li +1
Fine-grained entity typing is a challenging problem since it usually involves a relatively large tag set and may require to understand the context of the entity mention. In this pa…
Neural Aspect and Opinion Term Extraction with Mined Rules as Weak Supervision
Hongliang Dai, Yangqiu Song
Lack of labeled training data is a major bottleneck for neural network based aspect and opinion term extraction on product reviews. To alleviate this problem, we first propose an a…