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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 許永真(Jane Yung-Jen Hsu) | |
| dc.contributor.author | Ming-Tung Hong | en |
| dc.contributor.author | 洪明彤 | zh_TW |
| dc.date.accessioned | 2021-06-16T04:15:55Z | - |
| dc.date.available | 2014-09-18 | |
| dc.date.copyright | 2014-08-25 | |
| dc.date.issued | 2014 | |
| dc.date.submitted | 2014-08-20 | |
| dc.identifier.citation | Bibliography
[1] A. Agarwal, B. Xie, I. Vovsha, O. Rambow, and R. Passonneau. Sentiment analysis of twitter data. In Proceedings of the Workshop on Languages in Social Media, pages 30–38. Association for Computational Linguistics, 2011. [2] A. Aw, M. Zhang, J. Xiao, and J. Su. A phrase-based statistical model for sms text normalization. In Proceedings of the COLING/ACL on Main conference poster ses- sions, pages 33–40. Association for Computational Linguistics, 2006. [3] S.Bird,E.Klein,andE.Loper.NaturalLanguageProcessingwithPython.O’Reilly Media, 2009. [4] M.Choudhury,R.Saraf,V.Jain,A.Mukherjee,S.Sarkar,andA.Basu.Investigation and modeling of the structure of texting language. International Journal of Document Analysis and Recognition (IJDAR), 10(3-4):157–174, 2007. [5] E. Clark and K. Araki. Text normalization in social media: progress, problems and applications for a pre-processing system of casual english. Procedia-Social and Be- havioral Sciences, 27:2–11, 2011. [6] P.CookandS.Stevenson.Anunsupervisedmodelfortextmessagenormalization.In Proceedings of the Workshop on Computational Approaches to Linguistic Creativity, pages 71–78. Association for Computational Linguistics, 2009. [7] D. Crystal. Texting. ELT journal, 62(1):77–83, 2008. 41 [8] S. Dow, A. Kulkarni, S. Klemmer, and B. Hartmann. Shepherding the crowd yields better work. In Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work, pages 1013–1022. ACM, 2012. [9] S.Gouws,D.Metzler,C.Cai,andE.Hovy.Contextualbearingonlinguisticvariation in social media. In Proceedings of the Workshop on Languages in Social Media, pages 20–29. Association for Computational Linguistics, 2011. [10] M. Kaufmann and J. Kalita. Syntactic normalization of twitter messages. In Inter- national conference on natural language processing, Kharagpur, India, 2010. [11] C. Kobus, F. Yvon, and G. Damnati. Normalizing sms: are two metaphors better thanone? InProceedingsofthe22ndInternationalConferenceonComputational Linguistics-Volume 1, pages 441–448. Association for Computational Linguistics, 2008. [12] E. Kouloumpis, T. Wilson, and J. Moore. Twitter sentiment analysis: The good the bad and the omg! ICWSM, 11:538–541, 2011. [13] E.LawandL.VonAhn.Input-agreement:anewmechanismforcollectingdatausing human computation games. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pages 1197–1206. ACM, 2009. [14] G. A. Miller. Wordnet: a lexical database for english. Communications of the ACM, 38(11):39–41, 1995. [15] C.-C.MusatThisone,A.Ghasemi,andB.Faltings.Sentimentanalysisusinganovel human computation game. In Proceedings of the 3rd Workshop on the People’s Web Meets NLP: Collaboratively Constructed Semantic Resources and their Applications to NLP, pages 1–9. Association for Computational Linguistics, 2012. [16] O. Owoputi, B. O’Connor, C. Dyer, K. Gimpel, N. Schneider, and N. A. Smith. Improved part-of-speech tagging for online conversational text with word clusters. In Proceedings of NAACL-HLT, pages 380–390, 2013. 42 [17] A. J. Quinn and B. B. Bederson. Human computation: a survey and taxonomy of a growing field. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pages 1403–1412. ACM, 2011. [18] K. Roschke. The text generation: Is English the next dead language? PhD thesis, Master’s thesis, Arizona State University, Tempe, AZ). Retrieved from http://mwtc. composing. org/grad/projects/roschke. pdf, 2008. [19] J. Ross, L. Irani, M. Silberman, A. Zaldivar, and B. Tomlinson. Who are the crowd- workers?: shifting demographics in mechanical turk. In CHI’10 Extended Abstracts on Human Factors in Computing Systems, pages 2863–2872. ACM, 2010. [20] N. Seemakurty, J. Chu, L. Von Ahn, and A. Tomasic. Word sense disambiguation via human computation. In Proceedings of the acm sigkdd workshop on human computation, pages 60–63. ACM, 2010. [21] L. Von Ahn and L. Dabbish. Designing games with a purpose. Communications of the ACM, 51(8):58–67, 2008. [22] L. Wasden. Internet lingo dictionary: A parents guide to codes used in chat rooms, instant messaging, text messaging. Technical report, and blogs. Technical report, Attorney General, 2010. 43 | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/55665 | - |
| dc.description.abstract | Lingo is an emerging language on the Internet. To understand the meaning of lingo can help analyze the web content and understand various cultures in the online communities. However, providing a standardized definition remains difficult due to continuous changes made to its nature. We proposed Tranzzl!n9o, a crossword puzzle game for engaging crowds to translate Internet lingo. In our game, players provide explanations for lingo in parallel and iteratively verify the explanations from other players. We conducted experiments with 45 qualified workers to evaluate our design on Amazon Mechanical Turk. There are 138 explanations generated from 20 puzzles by 45 qualified players. Results show that we achieved 77.06% precision and 85.71% recall for collecting explanations of lingo. With at least twice agreements, we achieved 90.57% precision and 48.98% recall. Moreover, crowed-sourced explanations are very informative, not only explaining lingo itself but also containing lingo usage. Follow-up questionnaires show that over 60% of players like our game and would like to play it again. Considering weekly players, 75% of them said so. By keeping our lingo dictionary updated, we hope to support out-of-vocabulary issues in language processing and an annotated corpus of lingo for machine learning, and help Internet users better-understand lingo. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T04:15:55Z (GMT). No. of bitstreams: 1 ntu-103-R01922115-1.pdf: 2308333 bytes, checksum: 797ce38db3e8deac7a83e8cbf0b44018 (MD5) Previous issue date: 2014 | en |
| dc.description.tableofcontents | Contents
口試委員會審定書 iii 誌謝 v Abstract vii 1 Introduction 1 1.1 Motivation.................................. 1 1.2 TranslatingInternetLingo ......................... 2 1.3 ProblemDefinition ............................. 3 2 Related Work 7 2.1 InternetLingoDictionaryLookup ..................... 7 2.2 MachineComputationinTextNormalization . . . . . . . . . . . . . . . 8 2.2.1 SpellingCorrectionApproach ................... 8 2.2.2 MachineTranslationApproach................... 8 2.2.3 AutomaticSpeechRecognitionApproach . . . . . . . . . . . . . 9 2.3 Human 2.3.1 Jinx for Generating Word Sense Disambiguation Dataset . . . . . 10 2.3.2 GuesstimentforSentimentAnnotation. . . . . . . . . . . . . . . 10 3 Internet Lingo Translating System 11 3.1 SystemDesignIncentive .......................... 11 3.2 InternetLingoExtraction.......................... 13 ComputationinWordGames.................... 9 ix 3.2.1 LanguageDetection ........................ 13 3.2.2 Rule-basedFiltering ........................ 14 3.3 DictionarySetupandQuestionPool .................... 15 3.4 HumanComputation:WordGame..................... 15 3.4.1 PuzzleGenerator .......................... 16 3.4.2 GameWorkflow .......................... 17 3.5 HumanComputation:DesignFeatures................... 19 3.5.1 PlayerQualification ........................ 19 3.5.2 Two-stageAgreement ....................... 21 4 Experiments and Evaluation 23 4.1 SystemDeployment............................. 23 4.2 DataProfile................................. 24 4.2.1 TweetsDataStatistics ....................... 24 4.2.2 CrosswordPuzzleSetup ...................... 25 4.3 Experiments................................. 26 4.3.1 LessonLearnedfromPilotStudy ................. 26 4.3.2 DiscussionofCollectedExplanations . . . . . . . . . . . . . . . 27 4.4 Evaluation.................................. 29 4.4.1 GroundTruthCollection...................... 29 4.4.2 EvaluationMetrics ......................... 30 4.4.3 Evaluation:LingofyTask...................... 31 4.4.4 Evaluation:UnlingofyTask .................... 32 4.4.5 Evaluation:EngaginginTranzz!n9o. . . . . . . . . . . . . . . . 34 5 Conclusions and Future work 39 5.1 Conclusions................................. 39 5.2 Limitations ................................. 40 5.3 FutureWork................................. 40 Bibliography 41 x | |
| dc.language.iso | en | |
| dc.subject | 群眾運算 | zh_TW |
| dc.subject | 網路方言 | zh_TW |
| dc.subject | 網路方言 | zh_TW |
| dc.subject | 群眾運算 | zh_TW |
| dc.subject | Human Computation | en |
| dc.subject | Internet Lingo | en |
| dc.subject | Human Computation | en |
| dc.subject | Internet Lingo | en |
| dc.title | 群眾運算機制於翻譯網路方言之研究 | zh_TW |
| dc.title | A Human Computation Approach to English Translation of Internet Lingo | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 102-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 林守德(Shou-De Lin),陳昇瑋(Sheng-Wei Chen),蔡宗翰(Tzong-Han Tsai),林光龍 | |
| dc.subject.keyword | 群眾運算,網路方言, | zh_TW |
| dc.subject.keyword | Human Computation,Internet Lingo, | en |
| dc.relation.page | 43 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2014-08-20 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
| 顯示於系所單位： | 資訊工程學系 | |
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