Clustering-based query representations with a novel multi-intent loss and a concordance rate metric improve healthcare search intent classification on two real-world log datasets.
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GEN Encoder learns query intent embeddings from click logs as weak supervision and multi-task paraphrase training, outperforming prior methods on intent similarity and using nearest-neighbor search to cover half of unseen queries.
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Enhancing Healthcare Search Intent Recognition with Query Representation Learning and Session Context
Clustering-based query representations with a novel multi-intent loss and a concordance rate metric improve healthcare search intent classification on two real-world log datasets.
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Generic Intent Representation in Web Search
GEN Encoder learns query intent embeddings from click logs as weak supervision and multi-task paraphrase training, outperforming prior methods on intent similarity and using nearest-neighbor search to cover half of unseen queries.