Query Expansion with Locally-Trained Word Embeddings
Table 2: Retrieval results comparing query expansion based on various global and local embeddings. Bolded numbers indicate the best expansion in that class of embeddings. Wilcoxon signed rank test between bolded numbers indicates statistically significant improvements (p<0.05) for all collections.
['[EMPTY]', '[EMPTY]', 'global wiki+giga', 'global wiki+giga', 'global wiki+giga', 'global wiki+giga', 'global gnews', 'global target', 'local target', 'local giga', 'local wiki']
We find that embedding-based query expansion outperforms our query likelihood baseline across all conditions. When using the global embedding, the news corpora benefit from the various embeddings in different situations. Interestingly, for trec12, using an embedding trained on the target corpus significantly outperforms all other global embeddings, despite using substantially less data to estimate the model. While this performance may be due to the embedding having a tokenization consistent with the target corpus, it may also come from the fact that the corpus is more representative of the target documents than other embeddings which rely on online news or are mixed with non-news content. To some extent this supports our desire to move training closer to the target distribution.