Query Expansion with Locally-Trained Word Embeddings
Table 3: Kendall’s τ and Spearman’s ρ between improvement in NDCG@10 and local KL divergence with the corpus language model. The improvement is measured for the best local embedding over the best global embedding.
['[EMPTY]', '[ITALIC] τ', '[ITALIC] ρ']
The results support the hypothesis that local embeddings provide better similarity measures than global embeddings for query expansion. In order to understand why, we first compare the performance differences between local and global embeddings. We hypothesize that, when this value is high, the topic language is different from the corpus language and the global embedding will be inferior to the local embedding. We tested the rank correlation between this KL divergence and the relative performance of the local embedding with respect to the global embedding. Unfortunately, we find that the correlation is low, although it is positive across collections. Despite these strong results, we believe that there are still some open questions in this work. First, although local embeddings provide effectiveness gains, they can be quite inefficient compared to global embeddings. We believe that there is opportunity to improve the efficiency by considering offline computation of local embeddings at a coarser level than queries but more specialized than the corpus. If the retrieval algorithm is able to select the appropriate embedding at query time, we can avoid training the local embedding. Further theoretical and empirical analysis is necessary.