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Twitter 101 Useful Tools and Resources Toby Greenwalt, Theanalogdivide.Com on Twitter: @Theanalogdivide
Twitter 101 Useful Tools and Resources Toby Greenwalt, theanalogdivide.com On Twitter: @theanalogdivide So you’ve created a Twitter account, entered your profile information, and uploaded a photo. Now what? Here are a few tools for making the most of your time with the service. You’ll be one of the shining Twitteratti before you know it! Finding Friends and Followers Twitter works best when you have a healthy crowd to talk to and share ideas with. Here’s a few suggestions for expanding your network. If you build it, they will come: Many people will find your account by virtue of their own searches. Be warned that not all of these people are actually interested in what you have to say, or are possibly even real people. Raid your address book: Twitter can scan your address book to see if any of your contacts are on the service. This is a quick and easy way to see who’s out there. The Kevin Bacon method: Once you’ve found a few friends, you can look at their friends, and their friends, and their friends… Discover the tangled web we, um, tweave! Lists: Similar to the Twibes and WeFollow directories listed below, Lists are user-compiled directories of Tweet- ers based around a well defined subject. How many lists are there for your community? Search Tools If you’ve gone through your immediate contacts, there are a few web tools for finding like minds. Twitter Grader (grader.twitter.com): Once you’ve found out your grade, click on the Elite buttons to find out who the BTOCs (Big Twitterers on Campus) are. -
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 .................................................................................................................................................. -
Twitter Para Quien No Usa Twitter
Twitter ...para quien no usa Twitter Juan Diego Polo autor de wwwhatsnew.com descárgate este libro en www.bubok.com © Juan Diego Polo García 2009 [email protected] Licencia de uso Creative Commons, publicable, copiable, distribuíble de cualquier modo, NO editable, NO comercial, con obligación de citar al autor y la dirección http://wwwhatsnew.com Las marcas y logotipos aquí mostrados son marcas registradas y propiedad de sus respectivas compañías y sólo son usadas como referencia. Este libro es una publicación independiente y no está afiliada, autorizada, esponsorizada, o de cualquier otra manera aprobada por Twitter o cualquiera de las empresas nombradas en sus páginas. 1ª Edición Diseño de Portada: Lucas García - www.socialmood.com Impreso en España / Printed in Spain Impreso por Bubok 1 Índice de Contenido 1 - INTRODUCCIÓN..........................................................................5 1.1 - QUÉ ES TWITTER..............................................................................6 1.2 - QUIÉN ESCRIBE EN TWITTER............................................................10 1.3 - QUÉ SE PUEDE ESCRIBIR EN TWITTER...............................................13 1.4 - CÓMO SE DIVULGAN ENLACES EN TWITTER.......................................16 1.5 - CÓMO SE ENVÍAN MENSAJES A OTROS USUARIOS DE TWITTER............18 1.6 - CÓMO PUEDEN CLASIFICARSE LOS MENSAJES....................................22 2 - PARA QUÉ PODEMOS USAR TWITTER...........................................26 2.2 - PARA ENCONTRAR CLIENTES............................................................36 -
Social Media Compendium Oktober 2009
Social Media Compendium Oktober 2009 COMMUNITY PLATFORMS / SOCIAL NETWORKS NICHED COMMUNITIES BLOG PLATFORMS BLOG COMMUNITIES & TOOLS / FORUM BLOG SEARCH COMMENT / REPUTATION MICROMEDIA / MICROBLOGGING SOCIAL BOOKMARKING CROWDSOURCED CONTENT CUSTOMER SERVICE, REVIEWS TEXT & PRESENTATION PUBLISHING & SHARING IMAGE SHARING & HOSTING IMAGE SEARCH IMAGE EDITING MUSIC SHARING & STREAMING VIDEO PUBLISHING & SHARING INSTRUCTIONAL & EDUCATIONAL VIDEOS VIDEO SEARCH ENGINES VIDEO STREAMING FEEDS / NEWS AGGREGATOR SOCIAL AGGREGATOR / PROFILE MANAGER LOCATION!BASED EVENTS DIRECT COMMUNICATION "IM / SMS / VOICE# WIKIS COLLABORATIVE PLATFORMS PRODUCTIVITY TOOLS INFORMATION DATABASES / MONITORING MEDIA & COMMUNICATION BLOGS SEARCH ENGINES REAL!TIME SEARCH by Matthieu Hartig ■ [email protected] ■ @matthartig COMMUNITY PLATFORMS / SOCIAL NETWORKS facebook.com (2) Facebook is the world’s largest free-access social networking website. Users can join networks organized by city, workplace, school, and region to connect and interact with other people. People can also add friends and send them messages, and update their personal pro"les to notify friends. hi5.com (43) hi5 is an international social network with a local #avor. It enables members to stay connect- ed, share their lives, and learn what’s happening around them – through customizable pro"le pages, messaging, unlimited photo storage, hundreds of OpenSocial applications and more. friendster.com (117) Founded in 2002, Friendster is one of the web’s older social networking services. Adults, 16 and up can join and connect with friends, family, school, groups, activities and interests. $e site currently has over 50 million users. Over 90% of Friendster’s tra%c comes from Asia. tagged.com (109) Protecting the safety of their users is what makes Tagged di&erent from other social network- ing sites. -
Ruken C¸Akici
RUKEN C¸AKICI personal information Official Ruket C¸akıcı Name Born in Turkey, 23 June 1978 email [email protected] website http://www.ceng.metu.edu.tr/˜ruken phone (H) +90 (312) 210 6968 · (M) +90 (532) 557 8035 work experience 2010- Instructor, METU METU Research and Teaching duties 1999-2010 Research Assistant, METU — Ankara METU Teaching assistantship of various courses education 2002-2008 University of Edinburgh, UK Doctor of School: School of Informatics Philosophy Thesis: Wide-Coverage Parsing for Turkish Advisors: Prof. Mark Steedman & Prof. Miles Osborne 1999-2002 Middle East Technical University Master of Science School: Computer Engineering Thesis: A Computational Interface for Syntax and Morphemic Lexicons Advisor: Prof. Cem Bozs¸ahin 1995-1999 Middle East Technical University Bachelor of Science School: Computer Engineering projects 1999-2001 AppTek/ Lernout & Hauspie Inc Language Pairing on Functional Structure: Lexical- Functional Grammar Based Machine Translation for English – Turkish. 150000USD. · Consultant developer 2007-2011 TUB¨ MEDID Turkish Discourse Treebank Project, TUBITAK 1001 program (107E156), 137183 TRY. · Researcher · (Now part of COST Action IS1312 (TextLink)) 2012-2015 Unsupervised Learning Methods for Turkish Natural Language Processing, METU BAP Project (BAP-08-11-2012-116), 30000 TRY. · Primary Investigator 2013-2015 TwiTR: Turkc¸e¨ ic¸in Sosyal Aglarda˘ Olay Bulma ve Bulunan Olaylar ic¸in Konu Tahmini (TwiTR: Event detection and Topic identification for events in social networks for Turkish language), TUBITAK 1001 program (112E275), 110750 TRY. · Researcher· (Now Part of ICT COST Action IC1203 (ENERGIC)) 2013-2016 Understanding Images and Visualizing Text: Semantic Inference and Retrieval by Integrating Computer Vision and Natural Language Processing, TUBITAK 1001 program (113E116), 318112 TRY. -
Perceptions and Expressions of Social Presence During Conversations on Twitter
PERCEPTIONS AND EXPRESSIONS OF SOCIAL PRESENCE DURING CONVERSATIONS ON TWITTER A Thesis by KELLY MARIE PRITCHETT Submitted to the Office of Graduate Studies of Texas A&M University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE December 2011 Major Subject: Agricultural Leadership, Education, and Communications Perceptions and Expressions of Social Presence During Conversations on Twitter Copyright 2011 Kelly Marie Pritchett PERCEPTIONS AND EXPRESSIONS OF SOCIAL PRESENCE DURING CONVERSATIONS ON TWITTER A Thesis by KELLY MARIE PRITCHETT Submitted to the Office of Graduate Studies of Texas A&M University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE Approved by: Co-Chairs of Committee, Traci L. Naile Theresa Pesl Murphrey Committee Member R. Daniel Lineberger Head of Department, Jack Elliot December 2011 Major Subject: Agricultural Leadership, Education, and Communications iii ABSTRACT Perceptions and Expressions of Social Presence During Conversations on Twitter. (December 2011) Kelly Marie Pritchett, B.S., Texas A&M University Co-Chairs of Advisory Committee: Dr.Traci L. Naile Dr.Theresa Pesl Murphrey Computer-mediated environments such as social media create new social climates that impact communication interactions in un-mediated environments. This study examined social variables during conversations on Twitter through a qualitative document analysis that coded messages into affective, interactive or cohesive categories. Perceived social presence, participant satisfaction, and relationships between social presence and satisfaction among Twitter users during streaming conversations were examined through an online questionnaire that was created using qualtrics.com and made available to respondents over a one-week period. The researcher concluded that most social variables in the Twitter conversations of this study fall into the interactive social presence category. -
The Openhart 2013 Evalua on Workshop
Welcome to the OpenHaRT 2013 Evalua8on Workshop Informaon Technology Laboratory nist.gov/itl Informaon Access Division nist.gov/itl/iad Mark Przybocki, Mul(modal Informaon Group nist.gov/itl/iad/mig August 23rd, 2013 Washington D.C. , Omni Shoreham hotel The Mul8modal Informa8on Group’s Project Areas § Speech Recogni(on § Speaker Recogni(on § Dialog Management § Human Assisted Speaker Recogni(on § Topic Detec(on and Tracking § Speaker Segmentaon § Spoken Document Retrieval § Language Recogni(on § Voice Biometrics § ANSI/NIST-ITL Standard Voice Record § Tracking (Person/Object) § Text-to-Text § Event Detec(on § Speech-to-Text § Event Recoun(ng § Speech-to-Speech § Predic(ve Video Analy(cs § Image-to-Text § Metric Development § Named En(ty Iden(ficaon § (new) Data Analy(cs § Automac Content Extrac(on 2 Defini8on: MIG’s Evalua8on Cycle Evalua'on Driven Research NIST Data NIST Researchers Performance Planning NIST Core technology Assessment development Analysis and NIST NIST Workshop 3 NIST’s MT Program’s Legacy – Past 10 Years • 27 Evaluaon Events -- tracking the state-of-the-art in performance – (4) technology types text-text speech-text speech-speech Handwri[en_Images-text – (9) languages Arabic-2, Chinese, Dari, Farsi, Hindi, Korean, Pashto, Urdu, English • 11 genres of structured and unstructured content (nwire, web, Bnews, Bconv, food, speeches, editorials, handwri(ng-2, blogs, SMS, dialogs) • 60 Evaluaon Test Sets available to MT researchers (source, references, metrics, sample system output and official results for comparison) • Over 85 research groups >400 Primary Systems Evaluated AFRL – American Univ. Cairo – Apptek – ARL – BBN – BYU – Cambridge – Chinese Acad. Sci. – 5% 3% 1% CMU – Columbia Univ. – Fujitsu Research – 11% OpenMT Google – IBM – JHU – Kansas State – KCSL – Language Weaver – Microsoh Research – Ohio TIDES State – Oxford – Qatar – Queen Mary (London) – 23% 57% TRANSTAC RWTH Aachen – SAIC - Sakhr – SRI – Stanford – Systran – UMD – USC ISI – Univ. -
Statistical Machine Translation from English to Tuvan*
Statistical Machine Translation from English to Tuvan* Rachel Killackey, Swarthmore College rkillac [email protected] Linguistics Senior Thesis 2013 Abstract This thesis aims to describe and analyze findings of the Tuvan Machine Translation Project, which attempts to create a functional statistical machine translation (SMT) model between English and Tuvan, a minority language spoken in southern Siberia. Though most Tuvan speakers are also fluent in Russian, easily accessible SMT technology would allow for simpler English translation without the use of Russian as an intermediary language. The English to Tuvan half of the system that I examine makes consistent morphological errors, particularly involving the absence of the accusative suffix with the basic form -ni. Along with a typological analysis of these errors, I show that the introduction of novel data that corrects for the missing accusative suffix can improve the performance of an SMT system. This result leads me to conclude that SMT can be a useful avenue for efficient translation. However, I also argue that SMT may benefit from the incorporation of some linguistic knowledge such as morphological rules in the early steps of creating a system. 1. Introduction This thesis explores the field of machine translation (MT), the use of computers in rendering one natural language into another, with a specific focus on MT between English and Tuvan, a Turkic language spoken in south central Siberia. While MT is a growing force in the translation of major languages with millions of speakers such as French, Spanish, and Russian, minority and non-dominant languages with relatively few numbers of speakers have been largely ignored. -
Making Amharic to English Language Translator For
Hana Demas Making Amharic to English Language Translator for iOS Helsinki Metropolia University of Applied Sciences Degree Programme In Information Technology Thesis Date 5.5.2016 2 Author(s) Hana Belete Demas Title Amharic To English Language Translator For iOS Number of Pages 54 pages + 1 appendice Date 5 May 2016 Degree Information Technology Engineering Degree Programme Information Technology Specialisation option Software Engineering Instructor(s) Petri Vesikivi The purpose of this project was to build a language translator for Amharic-English language pair, which in the beginning of the project was not supported by any of the known translation systems. The goal of this project was to make a language translator application for Amharic English language pair using swift language for iOS platform. The project has two components. The first one is the language translator application described above and the second component is an integrated Amharic custom keyboard which makes the user able to type Amharic letters which are not supported by iOS 9 system keyboard. The Amharic language has more than 250 letters and numbers and they are represented using extended keys. The project was implemented using the Swift language. At the end of the project an iOS application to translate English to Amharic and vice versa was made. The translator applications uses the translation system which was built on the Microsoft Translator Hub and accessed using Microsoft Translator API. The application can be used to translate texts from Amharic to English or vice versa. Keywords API, iOS, Custom Keyboard, Swift, Microsoft Translator Hub 3 Contents 1. Introduction ............................................................................................................... 1 2. -
Proceedings of the 5Th Conference on Machine
EMNLP 2020 Fifth Conference on Machine Translation Proceedings of the Conference November 19-20, 2020 Online c 2020 The Association for Computational Linguistics Order copies of this and other ACL proceedings from: Association for Computational Linguistics (ACL) 209 N. Eighth Street Stroudsburg, PA 18360 USA Tel: +1-570-476-8006 Fax: +1-570-476-0860 [email protected] ISBN 978-1-948087-81-0 ii Introduction The Fifth Conference on Machine Translation (WMT 2020) took place on Thursday, November 19 and Friday, November 20, 2020 immediately following the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020). This is the fifth time WMT has been held as a conference. The first time WMT was held as a conference was at ACL 2016 in Berlin, Germany, the second time at EMNLP 2017 in Copenhagen, Denmark, the third time at EMNLP 2018 in Brussels, Belgium, and the fourth time at ACL 2019 in Florence, Italy. Prior to being a conference, WMT was held 10 times as a workshop. WMT was held for the first time at HLT-NAACL 2006 in New York City, USA. In the following years the Workshop on Statistical Machine Translation was held at ACL 2007 in Prague, Czech Republic, ACL 2008, Columbus, Ohio, USA, EACL 2009 in Athens, Greece, ACL 2010 in Uppsala, Sweden, EMNLP 2011 in Edinburgh, Scotland, NAACL 2012 in Montreal, Canada, ACL 2013 in Sofia, Bulgaria, ACL 2014 in Baltimore, USA, EMNLP 2015 in Lisbon, Portugal. The focus of our conference is to bring together researchers from the area of machine translation and invite selected research papers to be presented at the conference. -
Improvements in RWTH LVCSR Evaluation Systems for Polish, Portuguese, English, Urdu, and Arabic
Improvements in RWTH LVCSR Evaluation Systems for Polish, Portuguese, English, Urdu, and Arabic M. Ali Basha Shaik1, Zoltan Tuske¨ 1, M. Ali Tahir1, Markus Nußbaum-Thom1, Ralf Schluter¨ 1, Hermann Ney1;2 1Human Language Technology and Pattern Recognition – Computer Science Department RWTH Aachen University, 52056 Aachen, Germany 2Spoken Language Processing Group, LIMSI CNRS, Paris, France f shaik, tuske, tahir, nussbaum, schlueter, ney [email protected] Abstract acoustical mismatch between the training and testing can be re- In this work, Portuguese, Polish, English, Urdu, and Arabic duced for any target language, by exploiting matched data from automatic speech recognition evaluation systems developed by other languages [8]. Alternatively, a few attempts have been the RWTH Aachen University are presented. Our LVCSR sys- made to incorporate GMM within a framework of deep neu- tems focus on various domains like broadcast news, sponta- ral networks (DNN). The joint training of GMM and shallow neous speech, and podcasts. All these systems but Urdu are bottleneck features was proposed using the sequence MMI cri- used for Euronews and Skynews evaluations as part of the EU- terion, in which the derivatives of the error function are com- Bridge project. Our previously developed LVCSR systems were puted with respect to GMM parameters and applied the chain improved using different techniques for the aforementioned lan- rule to update the GMM simultaneously with the bottleneck guages. Significant improvements are obtained using multi- features [9]. On the other hand, a different GMM-DNN integra- lingual tandem and hybrid approaches, minimum phone error tion approach was proposed in [10] by taking advantage of the training, lexical adaptation, open vocabulary long short term softmax layer, which defines a log-linear model. -
Brizzly / Samjshah
Brizzly / samjshah http://brizzly.com/ Search Twitter or find people you follow samjshah help settings contact logout Tip: typing 'j' and 'k' scrolls tweets up and down. Home Results for "#needaredstamp" Save this search Trends and news Profile Mentions samjshah finish answering the question! Add another Twitter acct #needaredstamp [esp. in calc when asking to find eqn of tan line... students will stop after finding deriv.] Direct messages about 9 minutes ago via Brizzly Inbox #thoushallnot why? mathheadinc 0÷5≠ the empty set #needaredstamp Sent Xmas why? about 5 hours ago via TweetDeck Reply Retweet Create a direct message Avatar why? Copenhagen why? Lists mathheadinc 88÷0≠88 #needaredstamp about 5 hours ago via TweetDeck Reply Retweet #uksnow why? Add a new list RATM why? mathheadinc √(3^2+7^2) ≠ 10 #needaredstamp Iron Man 2 why? about 7 hours ago via TweetDeck Reply Retweet #omgfacts why? Collier why? nyates314 Yes indeed! RT @k8nowak @dgreenedcp @jbrtva @SweenWSweens That is a three part question, ? NOT multiple choice #needaredstamp about 7 hours ago via Power Twitter Reply Retweet Do you like Brizzly? Tell your friends & followers, and include an invitation. druinok Remember the example that we did in class that I told you to STAR and study? This is why! #needaredstamp about 7 hours ago via web Reply Retweet druinok I am totally cracking up at the #needaredstamp posts - you guys rock!!! Just what I needed this last week of school :) about 7 hours ago via web Reply Retweet k8nowak So embarrassing. So true. RT @SweenWSweens: That is a three part question, NOT multiple choice #needaredstamp about 7 hours ago via Brizzly Reply Retweet dgreenedcp @jbrtva RT @SweenWSweens That is a three part question, NOT multiple choice #needaredstamp // Snif.