ulanovav (Аннотации), страница 3
Описание файла
Файл "ulanovav" внутри архива находится в следующих папках: Аннотации, 2. PDF-файл из архива "Аннотации", который расположен в категории "". Всё это находится в предмете "английский язык" из 10 семестр (2 семестр магистратуры), которые можно найти в файловом архиве МГУ им. Ломоносова. Не смотря на прямую связь этого архива с МГУ им. Ломоносова, его также можно найти и в других разделах. .
Просмотр PDF-файла онлайн
Текст 3 страницы из PDF
It produces nearly the same results both for “ROMIP-GT merged”and “BL-GT filtered”. This experiment shows that “BL-GT filtered” contains enoughwords that can be used as classification features. However, it also contains commonwords that have low weight in the supervised classifier, which does not happen whenthis lexicon is used in vocabulary classification.4. ConclusionWe proposed a novel method for opinion lexicon projection from a source language to a target language with the use of a parallel corpus. The method was appliedto different datasets and evaluated against the baseline. The quality of created lexicons was evaluated in sentiment classification benchmark.
The experiments showedthat the lexicons are of high quality. They can be used for sentiment annotationof a corpus in a target language as well.Out future work is related to enhancement of the method and conducting moreexperiments. We plan to work with opinion phrases, investigate other translationContext-dependent opinion lexicon translation with the use of a parallel corpusoptions instead of the most probable ones. We will apply our method to other language pairs, apart from English-Russian. Additionally, it will be interesting to explorehow the method can be applied to other tasks, such as subjectivity lexicon projectionand, more general, multilingual projection of document features.References1.2.3.4.5.6.7.8.9.10.11.12.13.14.Banea C., Mihalcea R., Wiebe J., Hassan S.
Multilingual subjectivity analysis using machine translation. EMNLP ‘08: Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2008.Breck, E., Y Choi, and C. Cardie. Identifying expressions of opinion in context.In Proceedings of the International Joint Conference on Artificial Intelligence(IJCAI-2007), 2007.Carmen Banea, Rada Mihalcea, and Janyce Wiebe.Multilingual Sentiment andSubjectivity, in Multilingual Natural Language Processing, editors Imed Zitouniand Dan Bikel, Prentice Hall, 2011.Chatviorkin Ilya, Lukashevich Natalia.Automatic Extraction of Domain-SpecificOpinion Words. Proceedings of the International Conference Dialog, 2010.Chatviorkin Ilya, Braslavski Pavel, Lukashevich Natalia.Sentiment analysis trackat ROMIP 2011.
Proceedings of the International Conference Dialog, 2012.Dang Y., Zhang Y., Chen H. A lexicon-enhanced method for sentiment classification: An experiment on online product reviews, IEEE 2010.Ding, X., B. Liu, and P. Yu. A holistic lexicon-based approach to opinion mining.In Proceedings of the Conference on Web Search and Web Data Mining (WSDM2008), 2008.Eisele A., Chen Y. MultiUN: A Multilingual Corpus from United Nation Documents. In Language Resources and Evaluation, 2010.Hatzivassiloglou, V.
and K. McKeown. Predicting the semantic orientation of adjectives. In Proceedings of Annual Meeting of the Association for ComputationalLinguistics (ACL-1997), 1997.Hu, M. and B. Liu. Mining and summarizing customer reviews. In Proceedingsof ACM SIGKDD International Conference on Knowledge Discovery and DataMining (KDD-2004), 2004.J. Martineau and T. Finin.Delta TFIDF: An Improved Feature Space for SentimentAnalysis.
In Proceedings of the Third AAAI Internatonal Conference on Weblogsand Social Media, San Jose, CA, 2009.Liu, Bing.Web Data Mining. Exploring Hyperlinks, Contents, and Usage Data.2nd ed. 2011, XX, 622 p.Melville P., Gryc W., and Lawrence R. Sentiment analysis of blogs by combininglexical knowledge with text classification. KDD 2009.Mihalcea R., Banea C. and Wiebe J. Learning Multilingual Subjective Languagevia Cross-Lingual Projections, in Proceedings of the Association for Computational Linguistics (ACL 2007), Prague, June 2007.Ulanov A.
V., Sapozhnikov G. A.15. Och F. J., Ney H.A Systematic Comparison of Various Statistical Alignment Models, Computational Linguistics, volume 29, number 1, pp. 19–51 March 2003.16. Pak A., Paroubek P. Language independent approach to sentiment analysis(Limsi participation in romip’11) Proceedings of the International ConferenceDialog, 2012.17. Pang В., Lee L. Thumbs up? Sentiment Classification using Machine LearningTechniques, In Proc.
of the conference on the Empirical Methods 200218. Robert C. Moore. Fast and Accurate Sentence Alignment of Bilingual Corpora.In Proceedings of the 5th Conference of the Association for Machine Translationin the Americas on Machine Translation: From Research to Real Users (2002),pp. 135–14419. Steinberger J., Ebrahim M., Ehrmann M., Hurriyetoglu A., Kabadjov M., Lenkova P., Steinberger R., Tanev H., Vázquez S., Zavarella V. Creating SentimentDictionaries via Triangulation.
Decision Support Systems, May 2012.20. Steinberger J., Lenkova P., Kabadjov M., Steinberger R., van der Goot E. Multilingual Entity-Centered Sentiment Analysis Evaluated by Parallel Corpora. In Proceedings of the International Conference Recent Advances in Natural LanguageProcessing, 2011.21. Turney, P. T humbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews. In Proceedings of Annual Meeting of the Association for Computational Linguistics (ACL-2002), 2002..
