Survey and evaluation of web search engine hit counts as research tools in computational linguistics

Proposing a New Combined Indicator for Measuring Search Engine Performance and Evaluating Google, Yahoo, DuckDuckGo, and Bing Search Engines based on Combined Indicator

This study has developed a combined indicator to evaluate the performance of different search engines. Documentary analysis, survey, and evaluative methods are employed in the present study. The research was conducted in two stages. First, a combined indicator was designed to measure search engines. To this end, 72 criteria for measuring the performance of search engines were identified, out of which 22 criteria were selected. Accordingly, 10 criteria were selected in six general classes through a survey of subject matter experts. Validation of our proposed combined indicator was obtained by Delphi method and using the opinions of experts in the fields of information science and information system. Second, web search engines were evaluated based on the proposed combined indicator. The statistical population of this part of the research consisted of two categories: (1) general web search engines, and (2) general subjects. The sample size of the first category contained four search engines Yahoo, Google, DuckDuckGo, and Bing, and the second category involved 40 search terms under 10 general categories. The results showed that the combined indicator had six general criteria: (1) relevance, (2) ranking, (3) novelty ratio, (4) coverage ratio, (5) ratio of unrelated documents, and (6) proportion of duplication hits. According to this indicator, Google is at the top, followed by Bing. This study proposes a new indicator for evaluating search engine performance, which can measure the efficiency of search engines. Therefore, its use to measure the performance of search engines is recommended to researchers and search engine developers.

A Novel Deep Auto-Encoder Based Linguistics Clustering Model for Social Text

The wide adoption of media and social media has increased the amount of digital content to an enormous level. Natural language processing (NLP) techniques provide an opportunity to extract and explore meaningful information from a large amount of text. Among natural languages, Urdu is one of the widely used languages worldwide for spoken and written communications. Due to its wide adopt-ability, digital content in the Urdu language is increasing briskly, especially with social media and online NEWS feeds. Government agencies and advertisers must filter and understand the content to analyze the trends and cohorts in their interest and national prerogative. Clustering is considered a baseline and one of the first steps in natural language understanding. There are many state-of-the-art clustering techniques specifically for English, French, and Arabic, but no significant research has been conducted in Urdu language processing. Doing it for short text segments is challenging because of limited features and the absence of meaningful language discourse and nuance. Many rule-based NLP techniques are adopted to overcome these issues, relying on human-designed features and rules. Therefore, these methods do not promise remarkable results. Alongside NLP, deep learning techniques are pretty efficient in capturing contextual information with minimal noise compared to other traditional methods. By taking on this challenging job, we develop a deep learning-based technique for Urdu short text clustering for the very first time without a human-designed feature. In this paper, we propose a method of short text clustering using a deep neural network that automatically learns feature representations and clustering assignments simultaneously. This method learns clustering objectives by converting the high dimensional feature space to a low dimensional feature space. Our experiments on the Urdu NEWS headlines dataset show remarkable results compared to state-of-the-art methods.

Target specific mining of COVID-19 scholarly articles using one-class approach

The novel coronavirus disease 2019 (COVID-19) began as an outbreak from epicentre Wuhan, People's Republic of China in late December 2019, and till June 27, 2020 it caused 9,904,906 infections and 496,866 deaths worldwide. The world health organization (WHO) already declared this disease a pandemic. Researchers from various domains are putting their efforts to curb the spread of coronavirus via means of medical treatment and data analytics. In recent years, several research articles have been published in the field of coronavirus caused diseases like severe acute respiratory syndrome (SARS), middle east respiratory syndrome (MERS) and COVID-19. In the presence of numerous research articles, extracting best-suited articles is time-consuming and manually impractical. The objective of this paper is to extract the activity and trends of coronavirus related research articles using machine learning approaches to help the research community for future exploration concerning COVID-19 prevention and treatment techniques. The COVID-19 open research dataset (CORD-19) is used for experiments, whereas several target-tasks along with explanations are defined for classification, based on domain knowledge. Clustering techniques are used to create the different clusters of available articles, and later the task assignment is performed using parallel one-class support vector machines (OCSVMs). These defined tasks describes the behavior of clusters to accomplish target-class guided mining. Experiments with original and reduced features validate the performance of the approach. It is evident that the k-means clustering algorithm, followed by parallel OCSVMs, outperforms other methods for both original and reduced feature space.

Google Books, Scopus, Microsoft Academic and Mendeley for impact assessment of doctoral dissertations: A multidisciplinary analysis of the UK

A research doctorate normally culminates in publishing a dissertation reporting a substantial body of novel work. In the absence of a suitable citation index, this article explores the relative merits of alternative methods for the large-scale assessment of dissertation impact, using 150,740 UK doctoral dissertations from 2009–2018. Systematic methods for this were designed for Google Books, Scopus, Microsoft Academic, and Mendeley. Less than 1 in 8 UK doctoral dissertations had at least one Scopus (12%), Microsoft Academic (11%), or Google Books citation (9%), or at least one Mendeley reader (5%). These percentages varied substantially by subject area and publication year. Google Books citations were more common in the Arts and Humanities (18%), whereas Scopus and Microsoft Academic citations were more numerous in Engineering (24%). In the Social Sciences, Google Books (13%) and Scopus (12%) citations were important and in Medical Sciences, Scopus and Microsoft Academic citations to dissertations were rare (6%). Few dissertations had Mendeley readers (from 3% in Science to 8% in the Social Sciences) and further analysis suggests that Google Scholar finds more citations, but does not report information about all dissertations within a repository and is not a practical tool for large-scale impact assessment