Comparison of Paragram and Glove Results for Similarity Benchmarks

Dutkiewicz, Jakub; Jędrzejek, Czesław

doi:10.1007/978-3-319-98678-4_25

Cited by 1 publication

(1 citation statement)

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“…Much better results are obtained when sense disambiguation ( 44 ) and hubness reduction is applied to the vector space. For similarity tasks, the results in ( 45 ), where three different corrections to word2vec were used (retrofit, hubness removal and ranking type similarity), are up to 30% better than with the other method ( 15 ). Such a method (enhanced to relatedness) could allow direct comparison of query and target terms.…”

Section: Discussionmentioning

confidence: 99%

Baseline and extensions approach to information retrieval of complex medical data: Poznan's approach to the bioCADDIE 2016

2018

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Information retrieval from biomedical repositories has become a challenging task because of their increasing size and complexity. To facilitate the research aimed at improving the search for relevant documents, various information retrieval challenges have been launched. In this article, we present the improved medical information retrieval systems designed by Poznan University of Technology and Poznan University of Medical Sciences as a contribution to the bioCADDIE 2016 challenge—a task focusing on information retrieval from a collection of 794 992 datasets generated from 20 biomedical repositories. The system developed by our team utilizes the Terrier 4.2 search platform enhanced by a query expansion method using word embeddings. This approach, after post-challenge modifications and improvements (with particular regard to assigning proper weights for original and expanded terms), allowed us achieving the second best infNDCG measure (0.4539) compared with the challenge results and infAP 0.3978. This demonstrates that proper utilization of word embeddings can be a valuable addition to the information retrieval process. Some analysis is provided on related work involving other bioCADDIE contributions. We discuss the possibility of improving our results by using better word embedding schemes to find candidates for query expansion. Database URL: https://biocaddie.org/benchmark-data

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Section: Discussionmentioning

confidence: 99%