Search Results for author: Lana Yeganova

Found 12 papers, 2 papers with code

Measuring the relative importance of full text sections for information retrieval from scientific literature.

no code implementations NAACL (BioNLP) 2021 Lana Yeganova, Won Gyu Kim, Donald Comeau, W John Wilbur, Zhiyong Lu

In this work we establish the connection between the BM25 score of a query term appearing in a section of a full text document and the probability of that document being clicked or identified as relevant.

Information Retrieval Retrieval

BioCPT: Contrastive Pre-trained Transformers with Large-scale PubMed Search Logs for Zero-shot Biomedical Information Retrieval

1 code implementation2 Jul 2023 Qiao Jin, Won Kim, Qingyu Chen, Donald C. Comeau, Lana Yeganova, John Wilbur, Zhiyong Lu

Experimental results show that BioCPT sets new state-of-the-art performance on five biomedical IR tasks, outperforming various baselines including much larger models such as GPT-3-sized cpt-text-XL.

Biomedical Information Retrieval Contrastive Learning +4

Navigating the landscape of COVID-19 research through literature analysis: A bird's eye view

no code implementations7 Aug 2020 Lana Yeganova, Rezarta Islamaj, Qingyu Chen, Robert Leaman, Alexis Allot, Chin-Hsuan Wei, Donald C. Comeau, Won Kim, Yifan Peng, W. John Wilbur, Zhiyong Lu

In this study we analyze the LitCovid collection, 13, 369 COVID-19 related articles found in PubMed as of May 15th, 2020 with the purpose of examining the landscape of literature and presenting it in a format that facilitates information navigation and understanding.

Clustering named-entity-recognition +2

PDC -- a probabilistic distributional clustering algorithm: a case study on suicide articles in PubMed

no code implementations4 Dec 2019 Rezarta Islamaj, Lana Yeganova, Won Kim, Natalie Xie, W. John Wilbur

In this work, we present PDC (probabilistic distributional clustering), a novel algorithm that, given a document collection, computes disjoint term sets representing topics in the collection.


MeSH-based dataset for measuring the relevance of text retrieval

no code implementations WS 2018 Won Gyu Kim, Lana Yeganova, Donald Comeau, W. John Wilbur, Zhiyong Lu

Creating simulated search environments has been of a significant interest in infor-mation retrieval, in both general and bio-medical search domains.

Information Retrieval Retrieval +1

SingleCite: Towards an improved Single Citation Search in PubMed

no code implementations WS 2018 Lana Yeganova, Donald C. Comeau, Won Kim, W. John Wilbur, Zhiyong Lu

A search that is targeted at finding a specific document in databases is called a Single Citation search.

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