PMC:7799291 / 38730-39806 JSONTXT

{"target":"http://pubannotation.org/docs/sourcedb/PMC/sourceid/7799291","sourcedb":"PMC","sourceid":"7799291","source_url":"https://www.ncbi.nlm.nih.gov/pmc/7799291","text":"Search systems provide search experiences in which users issue queries expressing informational needs that the system satisfies with a returned collection of relevant documents. Queries can be collections of keyphrases, similar to those supported by traditional search engines like Google or PubMed. Indexing and retrieval can be implemented using open-source tools like Anserini [109] or commercial software like Amazon Kendra (https://aws.amazon.com/kendra/) or Azure Cognitive Search (https://azure.microsoft.com/en-us/services/search/). Systems like COVID papers browser (Row S2), CoronaSearch (Row S6) and CovidScholar (Row S21) compute embeddings for queries and paper text spans (i.e. sentences or entities) and retrieve documents containing nearest-neighbor spans as results. Some systems constrain the query vocabulary to entities in a known KB (e.g. COVID-19 Navigator (Row S14) allows query terms in the form of UMLS concepts). SPIKE-CORD [88] (Row S15) supports specification of regular expression-like patterns to afford users greater control over search results.","tracks":[]}