Papers with labelling functions
skweak: Weak Supervision Made Easy for NLP (2021.acl-demo)
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| Challenge: | skweak is a Python-based toolkit for NLP developers to use weak supervision . labelled data remains a scarce resource in many practical NLP scenarios . |
| Approach: | They present a Python-based toolkit that allows NLP developers to use weak supervision . skweak is designed to facilitate the use of weak supervision for NLP tasks . |
| Outcome: | skweak is a Python-based toolkit that facilitates weak supervision . the toolkit provides a simple interface to apply labels to a large corpus of text data . |
Named Entity Recognition without Labelled Data: A Weak Supervision Approach (2020.acl-main)
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| Challenge: | Named Entity Recognition (NER) performance often degrades when applied to target domains that differ from the texts observed during training. |
| Approach: | They propose a method to learn NER models in the absence of labelled data through weak supervision by using a broad spectrum of labelling functions to automatically annotate texts from the target domain. |
| Outcome: | The proposed approach improves on two English datasets and shows that it improves by 7 percentage points on entity-level F1 scores compared to an out-of-domain neural NER model. |