Tl;DR: This article presents a brief description of the rationale and structure of TREC-COVID, a still-ongoing IR evaluation. TREC-COVID is creating a new paradigm for search evaluation in rapidly evolving crisis scenarios.
Tl;DR: SciSight is a novel framework for exploratory search of COVID-19 research that integrates two key capabilities: first, exploring interactions between biomedical facets (e.g., proteins, genes, drugs, diseases, patient characteristics); and second, discovering groups of researchers and how they are co... nnected.
Tl;DR: A novel, unsupervised method for extracting scientific concepts from papers, based on the intuition that each scientific concept is likely to be introduced or popularized by a single paper that is disproportionately cited by subsequent papers mentioning the concept.
Tl;DR: 2020 is the year of search for Semantic Scholar, a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. One of our biggest endeavors this year is to improve the relevance of our search engine, and my mission beginning at the start of the year was to figure o... ut how to use about 3 years of search log data to build a better search ranker.
Tl;DR: We present a SLDEDGE, a search system that utilizes SciBERT to effectively re-rank articles related to SARS-CoV-2. SLEDGE achieves state-of-the-art results on the TREC covid search round 1 benchmark.
Tl;DR: The Covid-19 Open Research Dataset (CORD-19) is a growing 1 resource of scientific papers on Covid-19 and related historical coronavirus research. CORD-19 is designed to facilitate the development of text mining and information retrieval systems over its rich collection of metadata and structured fu... ll text papers.
Tl;DR: TREC-COVID is a community evaluation designed to build a test collection that captures the information needs of biomedical researchers using the scientific literature during a pandemic.
Tl;DR: We introduce GrapAL (Graph database of Academic Literature), a versatile tool for exploring and investigating scientific literature which satisfies a variety of use cases and information needs requested by researchers.
Tl;DR: We embed a given query document into a vector space, then use its nearest neighbors as candidates, and rerank the candidates using a discriminative model trained to distinguish between observed and unobserved citations.