Content-Based Table Retrieval for Web Queries
Table 3: Performance on WebQueryTable dataset with different aspects.
['Setting', 'Feature MAP', 'Feature P@1', 'NeuralNet MAP', 'NeuralNet P@1']
We further investigate the effects of headers, cells and caption for table retrieval on WebQueryTable. We first use each aspect separately and then increasingly combine different aspects. We can find that in general the performance of an aspect in designed features is consistent with its performance in neural networks. Caption is the most effective aspect on WebQueryTable. This is reasonable as we find that majority of the queries are asking about a list of objects, such as “polish rivers”, “world top 5 mountains” and “list of american cruise lines”. These intentions are more likely to be matched in the caption of a table. Combining more aspects could get better results. Using cells, headers and caption simultaneously gets the best results.