Ranking-based Fusion Algorithms for Extreme Multi-label Text Classification (XMTC)
Journal:
arXiv
Published Date:
Jul 4, 2025
Abstract
In the context of Extreme Multi-label Text Classification (XMTC), where
labels are assigned to text instances from a large label space, the long-tail
distribution of labels presents a significant challenge. Labels can be broadly
categorized into frequent, high-coverage \textbf{head labels} and infrequent,
low-coverage \textbf{tail labels}, complicating the task of balancing
effectiveness across all labels. To address this, combining predictions from
multiple retrieval methods, such as sparse retrievers (e.g., BM25) and dense
retrievers (e.g., fine-tuned BERT), offers a promising solution. The fusion of
\textit{sparse} and \textit{dense} retrievers is motivated by the complementary
ranking characteristics of these methods. Sparse retrievers compute relevance
scores based on high-dimensional, bag-of-words representations, while dense
retrievers utilize approximate nearest neighbor (ANN) algorithms on dense text
and label embeddings within a shared embedding space. Rank-based fusion
algorithms leverage these differences by combining the precise matching
capabilities of sparse retrievers with the semantic richness of dense
retrievers, thereby producing a final ranking that improves the effectiveness
across both head and tail labels.
