scholarly journals Erratum to: Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Database ◽  
2020 ◽  
Vol 2020 ◽  
Author(s):  
Peter Brown ◽  
Yaoqi Zhou ◽  
Keyword(s):  
Literature Search ◽  
Biomedical Literature ◽  
Document Similarity ◽  
Similarity Detection

scholarly journals Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Database ◽  
2019 ◽  
Vol 2019 ◽  
Author(s):  
Peter Brown ◽  
Aik-Choon Tan ◽  
Mohamed A El-Esawi ◽  
Thomas Liehr ◽  
Oliver Blanck ◽  
...  
Keyword(s):  
Literature Search ◽  
Relevant Literature ◽  
Biomedical Literature ◽  
Medical Subject Headings ◽  
Document Similarity ◽  
Inverse Document Frequency ◽  
Research Fields ◽  
Experience Levels ◽  
Document Frequency ◽  
Systematic Biases

Abstract Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency–Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.

scholarly journals PubMed Labs: an experimental system for improving biomedical literature search

Database ◽  
2018 ◽  
Vol 2018 ◽  
Author(s):  
Nicolas Fiorini ◽  
Kathi Canese ◽  
Rostyslav Bryzgunov ◽  
Ievgeniia Radetska ◽  
Asta Gindulyte ◽  
...  
Keyword(s):  
Literature Search ◽  
Experimental System ◽  
Biomedical Literature

Optimizing Document Similarity Detection in Persian Information Retrieval

2010 ◽  
Vol 5 (2) ◽  
pp. 101-106 ◽  
Author(s):  
Omid Kashefi ◽  
Nina Mohseni ◽  
Behrouz Minaei
Keyword(s):  
Information Retrieval ◽  
Document Similarity ◽  
Similarity Detection

Efficient Document Similarity Detection Using Weighted Phrase Indexing

2016 ◽  
Vol 11 (5) ◽  
pp. 231-244
Author(s):  
Papias Niyigena ◽  
Zhang Zuping ◽  
Mansoor Ahmed Khuhro ◽  
Damien Hanyurwimfura
Keyword(s):  
Document Similarity ◽  
Similarity Detection

CTGA: Graph-based Biomedical Literature Search

2019 ◽  
Author(s):  
Tianwen Jiang ◽  
Zhihan Zhang ◽  
Tong Zhao ◽  
Bing Qin ◽  
Ting Liu ◽  
...  
Keyword(s):  
Literature Search ◽  
Biomedical Literature

scholarly journals Better synonyms for enriching biomedical search

2020 ◽  
Vol 27 (12) ◽  
pp. 1894-1902 ◽  
Author(s):  
Lana Yeganova ◽  
Sun Kim ◽  
Qingyu Chen ◽  
Grigory Balasanov ◽  
W John Wilbur ◽  
...  
Keyword(s):  
Gold Standard ◽  
Literature Search ◽  
Similarity Score ◽  
Isotonic Regression ◽  
Biomedical Literature ◽  
Cosine Similarity ◽  
Experimental Results ◽  
Word Embeddings ◽  
Embedding Method ◽  
Embedding Methods

Abstract Objective In a biomedical literature search, the link between a query and a document is often not established, because they use different terms to refer to the same concept. Distributional word embeddings are frequently used for detecting related words by computing the cosine similarity between them. However, previous research has not established either the best embedding methods for detecting synonyms among related word pairs or how effective such methods may be. Materials and Methods In this study, we first create the BioSearchSyn set, a manually annotated set of synonyms, to assess and compare 3 widely used word-embedding methods (word2vec, fastText, and GloVe) in their ability to detect synonyms among related pairs of words. We demonstrate the shortcomings of the cosine similarity score between word embeddings for this task: the same scores have very different meanings for the different methods. To address the problem, we propose utilizing pool adjacent violators (PAV), an isotonic regression algorithm, to transform a cosine similarity into a probability of 2 words being synonyms. Results Experimental results using the BioSearchSyn set as a gold standard reveal which embedding methods have the best performance in identifying synonym pairs. The BioSearchSyn set also allows converting cosine similarity scores into probabilities, which provides a uniform interpretation of the synonymy score over different methods. Conclusions We introduced the BioSearchSyn corpus of 1000 term pairs, which allowed us to identify the best embedding method for detecting synonymy for biomedical search. Using the proposed method, we created PubTermVariants2.0: a large, automatically extracted set of synonym pairs that have augmented PubMed searches since the spring of 2019.

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