Boosting Entity Recognition by leveraging Cross-task Domain Models for Weak Supervision

Entity Recognition (ER) is a common natural language processing task encountered in a number of real-world applications. For common domains and named entities such as places and organisations, there exists sufficient high quality annotated data and foundational models such as T5 and GPT-3.5 also provide highly accurate predictions. However, for niche domains such as e-commerce and medicine with specialized entity types, there is a paucity of labeled data since manual labeling of tokens is often time-consuming and expensive, which makes entity recognition challenging for such domains. Recent works such as NEEDLE [48] propose hybrid solutions to efficiently combine a small amount of strongly labeled (human-annotated) with a large amount of weakly labeled (distant supervision) data to yield superior performance relative to supervised training. The extensive noise in the weakly labeled data, however, remains a challenge. In this paper, we propose WeSDoM (Weak Supervision with Domain Models), which leverages pretrained encoder models from the same domain but different tasks to create domain ontologies that can enable the creation of less noisy weakly labeled data. Experiments on internal e-commerce and public biomedical NER datasets demonstrate that WeSDoM outperforms existing SOTA baselines by a significant margin. We achieve new SOTA F1 scores on two popular Biomedical NER datasets, BC5CDR-chem 94.27, BC5CDR-disease 91.23.