Improved biomedical term selection in pseudo relevance feedback

Asim, Muhammad; Wasim, Muhammad; Khan, Muhammad Usman Ghani; Mahmood, Waqar

doi:10.1093/database/bay056

Cited by 7 publications

(6 citation statements)

References 24 publications

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“…Most of the early methods for scientific literature search and recommendation use keyword-based retrieval methods to provide access to the documents (Ginsparg, 1994;. These methods suffer from the term mismatch problem, which is common in "bag-of-words" retrieval methods, but the issue is aggravated by the diversity of the scientific vocabulary (Jerome et al, 2001;Dinh & Tamine, 2011;Nabeel Asim et al, 2018). As the number of users grows, popular search engines can exploit interaction signals to learn better ranking models (Mohan et al, 2017;Fiorini et al, 2018b;a).…”

Section: Related Workmentioning

confidence: 99%

Navigation-Based Candidate Expansion and Pretrained Language Models for Citation Recommendation

Nogueira

Jiang

Cho

et al. 2020

Preprint

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Citation recommendation systems for the scientific literature, to help authors find papers that should be cited, have the potential to speed up discoveries and uncover new routes for scientific exploration. We treat this task as a ranking problem, which we tackle with a two-stage approach: candidate generation followed by reranking. Within this framework, we adapt to the scientific domain a proven combination based on "bag of words" retrieval followed by re-scoring with a BERT model. We experimentally show the effects of domain adaptation, both in terms of pretraining on in-domain data and exploiting in-domain vocabulary. In addition, we introduce a novel navigation-based document expansion strategy to enrich the candidate documents processed by our neural models. On three different collections from different scientific disciplines, we achieve the best-reported results in the citation recommendation task.

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Section: Related Workmentioning

confidence: 99%

Navigation-Based Candidate Expansion and Pretrained Language Models for Citation Recommendation

Nogueira

Jiang

Cho

et al. 2020

Preprint

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“…Various methods have been proposed to improve data mining through keyword expansion. Most notably, researchers have proposed term and document frequency-based methods [18] and their variations, such as Chi-square with document frequency [19]. Others have proposed vector similarity-based approaches including co-occurrence vectors [20] and word embedding approaches [21].…”

Section: Introductionmentioning

confidence: 99%

Keyword expansion techniques for mining social movement data on social media

Bozarth

Budak

2022

EPJ Data Sci.

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Political and social scientists have been relying extensively on keywords such as hashtags to mine social movement data from social media sites, particularly Twitter. Yet, prior work demonstrates that unrepresentative keyword sets can lead to flawed research conclusions. Numerous keyword expansion methods have been proposed to increase the comprehensiveness of keywords, but systematic evaluations of these methods have been lacking. Our paper fills this gap. We evaluate five diverse keyword expansion techniques (or pipelines) on five representative social movements across two distinct activity levels. Our results guide researchers who aim to use social media keyword searches to mine data. For instance, we show that word embedding-based methods significantly outperform other even more complex and newer approaches when movements are in normal activity periods. These methods are also less computationally intensive. More importantly, we also observe that no single pipeline can identify little more than half of all movement-related tweets when these movements are at their peak mobilization period offline. However, coverage can increase significantly when more than one pipeline is used. This is true even when the pipelines are selected at random.

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“…This opens up a range of possibilities such as facilitating rapid access to targeted data [ 1 ]. However, the challenge for health care professionals is to identify relevant documents in this ocean of data [ 2 , 3 ].…”

Section: Introductionmentioning

confidence: 99%

Designing Formulae for Ranking Search Results: Mixed Methods Evaluation Study

Douzé¹,

Pelayo²,

Messaadi³

et al. 2022

JMIR Hum Factors

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Background A major factor in the success of any search engine is the relevance of the search results; a tool should sort the search results to present the most relevant documents first. Assessing the performance of the ranking formula is an important part of search engine evaluation. However, the methods currently used to evaluate ranking formulae mainly collect quantitative data and do not gather qualitative data, which help to understand what needs to be improved to tailor the formulae to their end users. Objective This study aims to evaluate 2 different parameter settings of the ranking formula of LiSSa (the French acronym for scientific literature in health care; Department of Medical Informatics and Information), a tool that provides access to health scientific literature in French, to adapt the formula to the needs of the end users. Methods To collect quantitative and qualitative data, user tests were carried out with representative end users of LiSSa: 10 general practitioners and 10 registrars. Participants first assessed the relevance of the search results and then rated the ranking criteria used in the 2 formulae. Verbalizations were analyzed to characterize each criterion. Results A formula that prioritized articles representing a consensus in the field was preferred. When users assess an article’s relevance, they judge its topic, methods, and value in clinical practice. Conclusions Following the evaluation, several improvements were implemented to give more weight to articles that match the search topic and to downgrade articles that have less informative or scientific value for the reader. Applying a qualitative methodology generates valuable user inputs to improve the ranking formula and move toward a highly usable search engine.

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Improved biomedical term selection in pseudo relevance feedback

Cited by 7 publications

References 24 publications

Navigation-Based Candidate Expansion and Pretrained Language Models for Citation Recommendation

Navigation-Based Candidate Expansion and Pretrained Language Models for Citation Recommendation

Keyword expansion techniques for mining social movement data on social media

Designing Formulae for Ranking Search Results: Mixed Methods Evaluation Study

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