  The web contains a vast corpus of HTML tables. They can be used to provide
direct answers to many web queries. We focus on answering two classes of
queries with those tables: those seeking lists of entities (e.g., `cities in
california') and those seeking superlative entities (e.g., `largest city in
california'). The main challenge is to achieve high precision with significant
coverage. Existing approaches train machine learning models to select the
answer from the candidates; they rely on textual match features between the
query and the content of the table along with features capturing table
quality/importance. These features alone are inadequate for achieving the above
goals. Our main insight is that we can improve precision by (i) first
extracting intent (structured information) from the query for the above query
classes and (ii) then performing structure-aware matching (instead of just
textual matching) between the extracted intent and the candidates to select the
answer. We model (i) as a sequence tagging task. We leverage state-of-the-art
deep neural network models with word embeddings. The model requires large scale
training data which is expensive to obtain via manual labeling; we therefore
develop a novel method to automatically generate the training data. For (ii),
we develop novel features to compute structure-aware match and train a machine
learning model. Our experiments on real-life web search queries show that (i)
our intent extractor for list and superlative intent queries has significantly
higher precision and coverage compared with baseline approaches and (ii) our
table answer selector significantly outperforms the state-of-the-art baseline
approach. This technology has been used in production by Microsoft's Bing
search engine since 2016.
