L
Luca Soldaini
Researcher at Amazon.com
Publications - 56
Citations - 749
Luca Soldaini is an academic researcher from Amazon.com. The author has contributed to research in topics: Computer science & Sentence. The author has an hindex of 12, co-authored 40 publications receiving 466 citations. Previous affiliations of Luca Soldaini include University of Washington & Georgetown University.
Papers
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Proceedings ArticleDOI
Don’t Parse, Generate! A Sequence to Sequence Architecture for Task-Oriented Semantic Parsing
TL;DR: A unified architecture based on Sequence to Sequence models and Pointer Generator Network to handle both simple and complex queries is proposed and achieves state of the art performance on three publicly available datasets.
Proceedings ArticleDOI
On clinical decision support
TL;DR: This work investigates the utility of applying pseudo-relevance feedback (PRF), a query expansion method that performs well in keyword-based medical literature search to CDS search, and obtains statistically significant retrieval efficiency improvement in terms of nDCG, over the baseline.
Proceedings Article
SMHD: A Large-Scale Resource for Exploring Online Language Usage for Multiple Mental Health Conditions
TL;DR: The authors investigated the creation of high-precision patterns to identify self-reported diagnoses of nine different mental health conditions, and obtain high-quality labeled data without the need for manual labelling.
Proceedings ArticleDOI
Don't Parse, Generate! A Sequence to Sequence Architecture for Task-Oriented Semantic Parsing
TL;DR: This paper proposed a unified architecture based on Sequence to Sequence models and Pointer Generator Network to handle both simple and complex queries, which achieves state-of-the-art performance on three publicly available datasets (ATIS, SNIPS, Facebook TOP).
Journal ArticleDOI
Enhancing web search in the medical domain via query clarification
TL;DR: The utility of bridging the gap between layperson and expert vocabularies is investigated and the approach adds the most appropriate expert expression to queries submitted by users, a task the authors call query clarification.