  Search query specificity is broadly divided into two categories - Exploratory
or Lookup. If a query specificity can be identified at the run time, it can be
used to significantly improve the search results as well as quality of
suggestions to alter the query. However, with millions of queries coming every
day on a commercial search engine, it is non-trivial to develop a horizontal
technique to determine query specificity at run time. Existing techniques
suffer either from lack of enough training data or are dependent on information
such as query length or session information. In this paper, we show that such
methodologies are inadequate or at times misleading. We propose a novel
methodology, to overcome these limitations. First, we demonstrate a
heuristic-based method to identify Exploratory or Lookup intent queries at
scale, classifying millions of queries into the two classes with a high
accuracy, as shown in our experiments. Our methodology is not dependent on
session data or on query length. Next, we train a transformer-based deep neural
network to classify the queries into one of the two classes at run time. Our
method uses a bidirectional GRU initialized with pretrained BERT-base-uncased
embeddings and an augmented triplet loss to classify the intent of queries
without using any session data. We also introduce a novel Semi-Greedy Iterative
Training approach to fine-tune our model. Our model is deployable for real time
query specificity identification with response time of less than one
millisecond. Our technique is generic, and the results have valuable
implications for improving the quality of search results and suggestions.
