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This paper addresses the problem of selecting of a set of texts forannotation in text classification using retrieval methods when there are limitson the number of annotations due to constraints on human resources. Anadditional challenge addressed is dealing with binary categories that have asmall number of positive instances, reflecting severe class imbalance. In oursituation, where annotation occurs over a long time period, the selection oftexts to be annotated can be made in batches, with previous annotations guidingthe choice of the next set. To address these challenges, the paper proposesleveraging SHAP to construct a quality set of queries for Elasticsearch andsemantic search, to try to identify optimal sets of texts for annotation thatwill help with class imbalance. The approach is tested on sets of cue textsdescribing possible future events, constructed by participants involved instudies aimed to help with the management of obesity and diabetes. We introducean effective method for selecting a small set of texts for annotation andbuilding high-quality classifiers. We integrate vector search, semantic search,and machine learning classifiers to yield a good solution. Our experimentsdemonstrate improved F1 scores for the minority classes in binaryclassification.

Retrieval-based Text Selection for Addressing Class-Imbalanced Data in Classification