Lucas Liu

BERTifying the Hidden Markov Model for Multi-Source Weakly Supervised Named Entity Recognition

We study the problem of learning a named entity recognition (NER) tagger using noisy labels from multiple weak supervision sources. Though cheap to obtain, the labels from weak supervision sources are often incomplete, inaccurate, and contradictory, making it difficult to learn an accurate NER model…