Some Working Search Engine Examples
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Empirical Study on Media Monitoring and Internationalisation Resources
MULTISENSOR Mining and Understanding of multilinguaL contenT for Intelligent Sentiment Enriched coNtext and Social Oriented inteRpretation FP7-610411 D2.1 Empirical study on media monitoring and internationalisation resources Dissemination level: Public Contractual date of delivery: Month 6, 30 April 2014 Actual date of delivery: Month 6, 30 April 2014 Workpackage: WP2 Multilingual and multimedia content extraction Task: T2.1 Empirical study Type: Report Approval Status: Final Draft Version: 1.1 Number of pages: 172 Filename: D2.1_EmpiricalStudy_2014-04-30_v1.1.pdf Abstract This empirical study identifies the resources and the type of information that needs to be extracted in the project and their encoding types. In addition it reports information retrieval and crawling techniques that could be employed for the extraction of this information. The information in this document reflects only the author’s views and the European Community is not liable for any use that may be made of the information contained therein. The information in this document is provided as is and no guarantee or warranty is given that the information is fit for any particular purpose. The user thereof uses the information at its sole risk and liability. Page 1 Co-funded by the European Union Page 2 D2.1 – V1.1 History Version Date Reason Revised by 0.1 20/03/2014 Draft V. Aleksić (LT) 0.2 03/04/2014 Comments S. Vrochidis (CERTH), I. Arapakis (BM-Y!) 0.3 15/04/2014 Update V.Aleksić (LT) 0.4 16/04/2014 Document for internal review V.Aleksić (LT) 0.5 24/04/2014 Review A. -
Market Research SD-5 Gathering Information About Commercial Products and Services
Market Research SD-5 Gathering Information About Commercial Products and Services DEFENSE STANDARDIZATION PROGRA M JANUARY 2008 Contents Foreword 1 The Market Research Other Considerations 32 Background 2 Process 13 Amount of Information Strategic Market Research to Gather 32 What Is Market Research? 2 (Market Surveillance) 14 Procurement Integrity Act 32 Why Do Market Research? 2 Identify the Market or Market Paperwork Reduction Act 33 Segment of Interest 14 When Is Market Research Cost of Market Research 34 Done? 5 Identify Sources of Market Information 16 Who Should Be Involved In Market Research? 7 Collect Relevant Market Other Information Information 17 Technical Specialist 8 Document the Results 18 on Market Research 35 User 9 Logistics Specialist 9 Tactical Market Research Appendix A 36 (Market Investigation) 19 Testing Specialist 9 Types of Information Summarize Strategic Market Available on the Internet Cost Analyst 10 Research 19 Legal Counsel 10 Formulate Requirements 20 Appendix B 39 Contracting Officer 10 Web-Based Information Identify Sources of Sources Information 21 Guiding Principles 11 Collect Product or Service Appendix C 47 Examples of Tactical Start Early 11 Information from Sources 22 Collect Information from Information Define and Document Product or Service Users 26 Requirements 11 Evaluate the Data 27 Refine as You Proceed 12 Document the Results 30 Tailor the Investigation 12 Repeat as Necessary 12 Communicate 12 Involve Users 12 Foreword The Department of Defense (DoD) relies extensively on the commercial market for the products and services it needs, whether those products and services are purely commercial, modified for DoD use from commercial products and services, or designed specifically for DoD. -
Awareness and Utilization of Search Engines for Information Retrieval by Students of National Open University of Nigeria in Enugu Study Centre Library
University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln Library Philosophy and Practice (e-journal) Libraries at University of Nebraska-Lincoln 2020 Awareness and Utilization of Search Engines for Information Retrieval by Students of National Open University of Nigeria in Enugu Study Centre Library SUNDAY JUDE ONUH National Open University of Nigeria, [email protected] OGOEGBUNAM LOVETH EKWUEME National Open University of Nigeria, [email protected] Follow this and additional works at: https://digitalcommons.unl.edu/libphilprac Part of the Library and Information Science Commons ONUH, SUNDAY JUDE and EKWUEME, OGOEGBUNAM LOVETH, "Awareness and Utilization of Search Engines for Information Retrieval by Students of National Open University of Nigeria in Enugu Study Centre Library" (2020). Library Philosophy and Practice (e-journal). 4650. https://digitalcommons.unl.edu/libphilprac/4650 Awareness and Utilization of Search Engines for Information Retrieval by Students of National Open University of Nigeria in Enugu Study Centre Library By Jude Sunday Onuh Enugu Study Centre Library National Open University of Nigeria [email protected] & Loveth Ogoegbunam Ekwueme Department of Library and Information Science National Open University of Nigeria [email protected] Abstract This study dwelt on awareness and utilization of search engines for information retrieval by students of National Open University of Nigeria (NOUN) Enugu Study centre. Descriptive survey research was adopted for the study. Two research questions were drawn from the two purposes that guided the study. The population consists of 5855 undergraduate students of NOUN Enugu Study Centre. A sample size of 293 students was used as 5% of the entire population. -
Gender and Information Literacy: Evaluation of Gender Differences in a Student Survey of Information Sources
Gender and Information Literacy: Evaluation of Gender Differences in a Student Survey of Information Sources Arthur Taylor and Heather A. Dalal Information literacy studies have shown that college students use a va- riety of information sources to perform research and commonly choose Internet sources over traditional library sources. Studies have also shown that students do not appear to understand the information quality issues concerning Internet information sources and may lack the information literacy skills to make good choices concerning the use of these sources. No studies currently provide clear guidance on how gender might influ- ence the information literacy skills of students. Such guidance could help improve information literacy instruction. This study used a survey of college-aged students to evaluate a subset of student information literacy skills in relation to Internet information sources. Analysis of the data collected provided strong indications of gender differences in information literacy skills. Female respondents ap- peared to be more discerning than males in evaluating Internet sources. Males appeared to be more confident in the credibility and accuracy of the results returned by search engines. Evaluation of other survey responses strengthened our finding of gender differentiation in informa- tion literacy skills. ollege students today have come of age surrounded by a sea of information delivered from an array of sources available at their fingertips at any time of the day or night. Studies have shown that the most common source of information for college-aged students is the Internet. Information gathered in this environment is most likely found using a commercial search engine that returns sources of dubious quality using an unknown algorithm. -
How to Choose a Search Engine Or Directory
How to Choose a Search Engine or Directory Fields & File Types If you want to search for... Choose... Audio/Music AllTheWeb | AltaVista | Dogpile | Fazzle | FindSounds.com | Lycos Music Downloads | Lycos Multimedia Search | Singingfish Date last modified AllTheWeb Advanced Search | AltaVista Advanced Web Search | Exalead Advanced Search | Google Advanced Search | HotBot Advanced Search | Teoma Advanced Search | Yahoo Advanced Web Search Domain/Site/URL AllTheWeb Advanced Search | AltaVista Advanced Web Search | AOL Advanced Search | Google Advanced Search | Lycos Advanced Search | MSN Search Search Builder | SearchEdu.com | Teoma Advanced Search | Yahoo Advanced Web Search File Format AllTheWeb Advanced Web Search | AltaVista Advanced Web Search | AOL Advanced Search | Exalead Advanced Search | Yahoo Advanced Web Search Geographic location Exalead Advanced Search | HotBot Advanced Search | Lycos Advanced Search | MSN Search Search Builder | Teoma Advanced Search | Yahoo Advanced Web Search Images AllTheWeb | AltaVista | The Amazing Picture Machine | Ditto | Dogpile | Fazzle | Google Image Search | IceRocket | Ixquick | Mamma | Picsearch Language AllTheWeb Advanced Web Search | AOL Advanced Search | Exalead Advanced Search | Google Language Tools | HotBot Advanced Search | iBoogie Advanced Web Search | Lycos Advanced Search | MSN Search Search Builder | Teoma Advanced Search | Yahoo Advanced Web Search Multimedia & video All TheWeb | AltaVista | Dogpile | Fazzle | IceRocket | Singingfish | Yahoo Video Search Page Title/URL AOL Advanced -
Web Search Tutoring for the Local Community
Web search for local communities in the Highlands of Scotland: A self-tutoring guide MODULE III Alternatives to Google: some other search tools worth a try © Copyright Hans Zell Publishing Consultants 2011 Glais Bheinn, Lochcarron, Ross-shire IV54 8YB, Scotland, UK Email: [email protected] Web: www.hanszell.co.uk Web search for local communities in the Highlands of Scotland: A self-tutoring guide MODULE I How to get the most out of Google Web search MODULE II A concise guide to Google products, services, applications, and other offerings MODULE III Alternatives to Google: some other search tools worth a try MODULE IV The best of the Web: a guide to some of the most information-rich resources on the Internet 2 Introduction Google is a marvellous Web search tool and is as good as they get at present, but it is certainly not the only one. Other top search engines include Ask.com (aka as Ask Jeeves), Bing (formerly called MSN Search), and Yahoo! (and see General purpose, product, and visual search engines below). According to data published by Experian Hitwise http://www.hitwise.com/us/datacenter/main/dashboard-23984.html in June 2011, Google still heavily dominates the market with a share of about 68%, while the market share of Yahoo and Microsoft’s Bing currently is something just under 14% for both; Ask.com is in fourth place with around 2.6%, and AOL Search in fifth place with about 1.4%. The picture is roughly the same if ranked by number of visits, although Bing does better than Yahoo in this category. -
Final Study Report on CEF Automated Translation Value Proposition in the Context of the European LT Market/Ecosystem
Final study report on CEF Automated Translation value proposition in the context of the European LT market/ecosystem FINAL REPORT A study prepared for the European Commission DG Communications Networks, Content & Technology by: Digital Single Market CEF AT value proposition in the context of the European LT market/ecosystem Final Study Report This study was carried out for the European Commission by Luc MEERTENS 2 Khalid CHOUKRI Stefania AGUZZI Andrejs VASILJEVS Internal identification Contract number: 2017/S 108-216374 SMART number: 2016/0103 DISCLAIMER By the European Commission, Directorate-General of Communications Networks, Content & Technology. The information and views set out in this publication are those of the author(s) and do not necessarily reflect the official opinion of the Commission. The Commission does not guarantee the accuracy of the data included in this study. Neither the Commission nor any person acting on the Commission’s behalf may be held responsible for the use which may be made of the information contained therein. ISBN 978-92-76-00783-8 doi: 10.2759/142151 © European Union, 2019. All rights reserved. Certain parts are licensed under conditions to the EU. Reproduction is authorised provided the source is acknowledged. 2 CEF AT value proposition in the context of the European LT market/ecosystem Final Study Report CONTENTS Table of figures ................................................................................................................................................ 7 List of tables .................................................................................................................................................. -
How to Write a Scientific Report
How to Write an EEI Contents: 1. Formatting your report………………………………………………………….page 3 Grammar v Tense………………………. page 5 Data V Crap………………………………… page 5 Googling ……………………………………. page 6 Referencing………………………………… page 8 Bibliography………………………………. page 12 2. Planning your investigation…………………………………………………..page 14 Variables……………………………………… page 16 Assumptions……………………………….. page 16 Experimental Replication……………. page 17 Checklist for Experimental Design page 17 3. Writing your Report……………………………………………………………….page 17 Title ……………………………………………… page 19 Abstract ………………………………………. page 20 Introduction…………………………………. page 21 Hypothesis ………………………………….. page 22 Risk Assessment………………………….. page 23 Variables………………………………………. Page 24/25 Method…………………………………………. Page 26 Results…………………………………………. page 27 Discussion ………………………………….. page 28, 29, 30 Conclusion ………………………………….. page 31 Literature Cited / Bibliography ….. page 33 Appendices………………………………….. page 34 APPENDICIES Appendix 1 – Data Analysis Appendix 3 – Scientific Drawings Appendix 4 – Literature Reviews Appendix 5 – Example/model reports Appendix 6 – False Positive Data Analysis FORMATTING YOUR REPORT Before you start Grammar and Tense FORMATTING Data v Crap! Qualitative v Quantitative data „Googling‟ How to search online Referencing How to cite reference within your text Bibliography How to write a scientific bibliography Use past tense, third person when writing your report…. e.g. “The research into the corrosion of metals was performed to see if …..” not “We did the experiment to see if….” FORMATTING “It -
Multitasking Web Search on Alta Vista
1 Multitasking Web Search on Alta Vista Amanda Spink* & Bernard J. Jansen Minsoo Park School of Info Sciences and Jan Pedersen School of Information Technology Chief Scientist Sciences The Pennsylvania State Overture Web Search University of Pittsburgh University Division 610 IS Building, 135 N. 4P Thomas Building 1070 Arastradero Road Bellefield Avenue University Park PA 16802 Palo Alto, CA 94304 Pittsburgh PA 15260 Tel: (814) 865-6459 [email protected] Tel: (412) 624-9454 Fax: (814) 865-6424 Fax: (412) 648-7001 E-mail: [email protected] E-mail: [email protected],edu * To whom all correspondence should be addressed. Ozmutlu [8] show that IR searches often include Abstract multiple topics, during a single search session or A user’s single session with a Web search engine multitasking search. Spink, Batemen and Greisdorf may consist of seeking information on single or [9] found that eleven (3.8%) of the 287 Excite users multiple topics. Most Web search sessions consist of responding to a Web-based survey reported two queries of two words. We present findings from a multitasking searches. However, limited knowledge study of two-query search sessions on the Alta Vista exists on the characteristics and patterns of Web search engine to examine the degree of multitasking searches. Recent studies have examined multitasking search by typical web searchers. A multitasking searching on the Excite and sample of two-query length search sessions were AlltheWeb.com Web search engines [10, 11]. filtered from Alta Vista transaction logs from 2003. Ozmutlu, Ozmutlu and Spink [10] provide a Findings include: (1) 81% of two-query sessions detailed analysis of multitasking sessions on were multitasking searches, and (2) there are a broad AlltheWeb.com. -
The Deep Web: Surfacing Hidden Value
White Paper The Deep Web: Surfacing Hidden Value BrightPlanet.com LLC July 2000 The author of this study is Michael K. Bergman. Editorial assistance was provided by Mark Smither; analysis and retrieval assistance was provided by Will Bushee. This White Paper is the property of BrightPlanet.com LLC. Users are free to distribute and use it for personal use.. Some of the information in this document is preliminary. BrightPlanet plans future revisions as better information and documentation is obtained. We welcome submission of improved information and statistics from others involved with the “deep” Web. Mata Hari® is a registered trademark and BrightPlanet™, CompletePlanet™, LexiBot™, search filter™ and A Better Way to Search™ are pending trademarks of BrightPlanet.com LLC. All other trademarks are the respective property of their registered owners. © 2000 BrightPlanet.com LLC. All rights reserved. Summary BrightPlanet has uncovered the “deep” Web — a vast reservoir of Internet content that is 500 times larger than the known “surface” World Wide Web. What makes the discovery of the deep Web so significant is the quality of content found within. There are literally hundreds of billions of highly valuable documents hidden in searchable databases that cannot be retrieved by conventional search engines. This discovery is the result of groundbreaking search technology developed by BrightPlanet called a LexiBot™ — the first and only search technology capable of identifying, retrieving, qualifying, classifying and organizing “deep” and “surface” content from the World Wide Web. The LexiBot allows searchers to dive deep and explore hidden data from multiple sources simultaneously using directed queries. Businesses, researchers and consumers now have access to the most valuable and hard- to-find information on the Web and can retrieve it with pinpoint accuracy. -
Exploring Mood on the Web
ESSE: Exploring Mood on the Web Sara Owsley Sood and Lucy Vasserman Computer Science Department, Pomona College 185 East Sixth Street, Room 232 Claremont, CA 91711 [email protected], [email protected] Abstract Google or Yahoo! afford. Rather, it enables the user to Future machines will connect with users on an emotional browse their topically relevant search results by mood, level in addition to performing complex computations providing the user with a unique perspective on the topic at (Norman 2004). In this article, we present a system that hand. Consider a user wishing to read opinions about the adds an emotional dimension to an activity that Internet new president of the United States. Typing “President users engage in frequently, search. ESSE, which stands for Obama” into a Google search box will return (among other Emotional State Search Engine, is a web search engine that results), a few recent news stories about Obama, the goes beyond facilitating a user’s exploration of the web by Whitehouse’s website, as well as a wikipedia article about topic, as search engines such as Google or Yahoo! afford. him. Typing “President Obama” to a Google Blog Search Rather, it enables the user to browse their topically relevant box user a bit closer to their goal in that all of the results search results by mood, providing the user with a unique perspective on the topic at hand. Consider a user wishing to are indeed blogs (typically opinions) about Obama. read opinions about the new president of the United States. However, where blog search engines fall short is in Typing “President Obama” into a Google search box will providing users with a way to navigate and digest the return (among other results), a few recent news stories about vastness of the blogosphere, the incredible number of Obama, the Whitehouse’s website, as well as a wikipedia results for the query “President Obama” (approximately article about him. -
Lexical Innovation on the Internet - Neologisms in Blogs
Zurich Open Repository and Archive University of Zurich Main Library Strickhofstrasse 39 CH-8057 Zurich www.zora.uzh.ch Year: 2009 Lexical innovation on the internet - neologisms in blogs Smyk-Bhattacharjee, Dorota Abstract: Studien im Bereich des Sprachwandels beschreiben traditionellerweise diachronische Verän- derungen in den Kernsubsystemen der Sprache und versuchen, diese zu erklären. Obwohl ein Grossteil der Sprachwissenschaftler sich darüber einig ist, dass die aktuellen Entwicklungen in einer Sprache am klarsten im Wortschatz reflektiert werden, lassen die lexikographischen und morphologischen Zugänge zur Beobachtung des lexikalischen Wandels wichtige Fragen offen. So beschäftigen sich letztere typischer- weise mit Veränderungen, die schon stattgefunden haben, statt sich dem sich zum aktuellen Zeitpunkt vollziehenden Wandel zu widmen. Die vorliegende Dissertation bietet eine innovative Lösung zur Un- tersuchung des sich vollziehenden lexikalischen Wandels sowohl in Bezug auf die Datenquelle als auch bzgl. der verwendeten Methodologie. In den vergangenen 20 Jahren hat das Internet unsere Art zu leben, zu arbeiten und zu kommunizieren drastisch beeinflusst. Das Internet bietet aber auch eine Masse an frei zugänglichen Sprachdaten und damit neue Möglichkeiten für die Sprachforschung. Die in dieser Arbeit verwendeten Daten stammen aus einem Korpus englischsprachiger Blogs, eine Art Computer gestützte Kommunikation (computer-mediated communication, CMC). Blogs bieten eine neue, beispiel- lose Möglichkeit, Wörtern nachzuspüren zum Zeitpunkt, in der sie Eingang in die Sprache finden. Um die Untersuchung des Korpus zu vereinfachen, wurde eine Software mit dem Namen Indiana entwickelt. Dieses Instrument verbindet den Korpus basierten Zugang mit einer lexikographischen Analyse. Indiana verwendet eine Kombination von HTML-to-text converter, eine kumulative Datenbank und verschiede Filter, um potentielle Neologismen im Korpus identifizieren zu können.