Effects of Porting Essie Tokenization and Normalization to Solr.

Soumya Gayen, Deepak Gupta, Russell F Loane, Nicholas C Ide, Dina Demner-Fushman

Abstract read

In one paragraph

Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Soumya GayenLister Hill National Center for Biomedical Communications, U.S. National Library of Medicine, Bethesda, MD, USA.

Deepak GuptaLister Hill National Center for Biomedical Communications, U.S. National Library of Medicine, Bethesda, MD, USA.

Russell F LoaneLister Hill National Center for Biomedical Communications, U.S. National Library of Medicine, Bethesda, MD, USA.

Nicholas C IdeLister Hill National Center for Biomedical Communications, U.S. National Library of Medicine, Bethesda, MD, USA.

Dina Demner-FushmanLister Hill National Center for Biomedical Communications, U.S. National Library of Medicine, Bethesda, MD, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Search for information is now an integral part of healthcare. Searches are enabled by search engines whose objective is to efficiently retrieve the relevant information for the user query. When it comes to retrieving biomedical text and literature, Essie search engine developed at the National Library of Medicine (NLM) performs exceptionally well. However, Essie is a software system developed for NLM that has ceased development and support. On the other hand, Solr is a popular opensource enterprise search engine used by many of the world's largest internet sites, offering continuous developments and improvements along with the state-of-the-art features. In this paper, we present our approach to porting the key features of Essie and developing custom components to be used in Solr. We demonstrate the effectiveness of the added components on three benchmark biomedical datasets. The custom components may aid the community in improving search methods for biomedical text retrieval.

Indexed as

Information Storage and RetrievalSoftwareBenchmarkingHumansInternetNational Library of Medicine (U.S.)Search EngineUnited States

Identifiers

PMID38222430

PMCPMC10785910

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.