Luca Soldaini

Amazon, Georgetown University
0000-0001-6998-9863
https://soldaini.net
Publications26
h-index11
Citations362
Highly Influential Citations44
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  • Publications
  • Influence
QuickUMLS: a Fast, Unsupervised Approach for Medical Concept Extraction
Entity extraction is a fundamental step in many health informatics systems. In recent years, tools such as MetaMap and cTAKES have been widely used for medical concept extraction on medical… Expand
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SMHD: A Large-Scale Resource for Exploring Online Language Usage for Multiple Mental Health Conditions
TLDR
We introduce the SMHD (Self-reported Mental Health Diagnoses) dataset, a large dataset of social media posts from users with one or multiple mental health conditions along with matched control users. Expand
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Retrieving Medical Literature for Clinical Decision Support
TLDR
We apply query reformulation techniques to address literature search based on case reports. Expand
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Don’t Parse, Generate! A Sequence to Sequence Architecture for Task-Oriented Semantic Parsing
TLDR
We propose a unified approach to tackle semantic parsing for natural language understanding based on Sequence to Sequence models and a Pointer Generator Network to handle both simple and complex queries. Expand
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Query Reformulation for Clinical Decision Support Search
TLDR
We present a query reformulation approach that addresses the unique formulation of case reports, making them suitable to be used on a general purpose search engine. Expand
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On clinical decision support
TLDR
We investigate the utility of applying pseudo-relevance feedback (PRF), a query expansion method that performs well in keyword-based medical literature search to CDS search. Expand
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Enhancing web search in the medical domain via query clarification
TLDR
We investigate the utility of bridging the gap between layperson and expert vocabularies; our approach adds the most appropriate expert expression to queries submitted by users, a task we call query clarification. Expand
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The Cascade Transformer: an Application for Efficient Answer Sentence Selection
TLDR
In this paper, we introduce the Cascade Transformer, a simple yet effective technique to adapt transformer-based models into a cascade of rankers. Expand
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Learning to Rank for Consumer Health Search: A Semantic Approach
TLDR
We present a Learning to Rank system that uses a novel set of syntactic and semantic features to improve consumer health search. Expand
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Characterizing Question Facets for Complex Answer Retrieval
TLDR
We present two novel approaches to Complex Answer Retrieval (CAR) based on the observation that question facets can vary in utility: from structural (facets that can apply to many similar topics, such as 'History') to topical. Expand
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