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

TL;DR

This paper proposes a retrieval-based method using SHAP and semantic search to select texts for annotation, improving classifier performance on imbalanced binary classification tasks in health-related datasets.

Contribution

It introduces a novel approach combining SHAP, vector search, and semantic search for effective text selection in class-imbalanced scenarios, with demonstrated improvements.

Findings

Improved F1 scores for minority classes.

Effective selection of annotation sets.

Enhanced classifier performance on imbalanced data.

Abstract

This paper addresses the problem of selecting of a set of texts for annotation in text classification using retrieval methods when there are limits on the number of annotations due to constraints on human resources. An additional challenge addressed is dealing with binary categories that have a small number of positive instances, reflecting severe class imbalance. In our situation, where annotation occurs over a long time period, the selection of texts to be annotated can be made in batches, with previous annotations guiding the choice of the next set. To address these challenges, the paper proposes leveraging SHAP to construct a quality set of queries for Elasticsearch and semantic search, to try to identify optimal sets of texts for annotation that will help with class imbalance. The approach is tested on sets of cue texts describing possible future events, constructed by participants involved in studies aimed to help with the management of obesity and diabetes. We introduce an effective method for selecting a small set of texts for annotation and building high-quality classifiers. We integrate vector search, semantic search, and machine learning classifiers to yield a good solution. Our experiments demonstrate improved F1 scores for the minority classes in binary classification.