The use of dense label embeddings in extreme multi-label text classification, as proposed in the MatchXML framework, has a significant impact on overall performance compared to traditional methods. Traditional approaches often rely on sparse TF-IDF features for generating label embeddings, which can have limitations such as inefficiency in processing large sparse vectors and dependency on the availability of TF-IDF features. In contrast, dense label embeddings learned through techniques like label2vec offer several advantages.
Firstly, dense embeddings capture semantic relationships more effectively than sparse representations. By training semantic dense label embeddings using models like Skip-gram, MatchXML is able to learn high-quality representations that encode meaningful relationships between labels. These dense vectors are more efficient for downstream machine learning algorithms to process due to their compact size and ability to capture complex relationships.
Secondly, the use of dense label embeddings enables improved performance in constructing Hierarchical Label Trees (HLT). The HLT plays a crucial role in reducing computational complexity by clustering semantically similar labels together. With high-quality dense label embeddings from MatchXML's approach, the HLT construction is more effective at grouping related labels and optimizing classification accuracy.
Overall, leveraging dense label embeddings enhances the quality of representation learning in extreme multi-label text classification tasks, leading to improved accuracies and efficiency compared to traditional methods relying solely on sparse features.