1 citations · 2 across the 4 of their papers we have counts for
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A Confidence-based Partial Label Learning Model for Crowd-Annotated Named Entity Recognition
Limao Xiong, Jie Zhou, Qunxi Zhu +7
Existing models for named entity recognition (NER) are mainly based on large-scale labeled datasets, which always obtain using crowdsourcing. However, it is hard to obtain a unifie…
MINER: Improving Out-of-Vocabulary Named Entity Recognition from an Information Theoretic Perspective
Xiao Wang, Shihan Dou, Limao Xiong +6
NER model has achieved promising performance on standard NER benchmarks. However, recent studies show that previous approaches may over-rely on entity mention information, resultin…
Multi-View Active Learning for Short Text Classification in User-Generated Data
Payam Karisani, Negin Karisani, Li Xiong
Mining user-generated data often suffers from the lack of enough labeled data, short document lengths, and the informal user language. In this paper, we propose a novel active lear…
View Distillation with Unlabeled Data for Extracting Adverse Drug Effects from User-Generated Data
Payam Karisani, Jinho D. Choi, Li Xiong
We present an algorithm based on multi-layer transformers for identifying Adverse Drug Reactions (ADR) in social media data. Our model relies on the properties of the problem and t…