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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1...46 47 48 49 50 51 52 53 54 Hits 981 – 1.000 of 1.080 981 Improving neural language models on low-resource creole languages Schieferstein, Sarah. - 2018 BASE Show details 982 Computational Approaches for Analyzing Social Support in Online Health Communities Khan Pour, Hamed. - : University of North Texas, 2018 BASE Show details 983 A Framework to Understand Emoji Meaning: Similarity and Sense Disambiguation of Emoji using EmojiNet Wijeratne, Sanjaya In: Browse all Theses and Dissertations (2018) BASE Show details 984 Framework for Sentiment Classification for Morphologically Rich Languages: A Case Study for Sinhala Medagoda, Nishantha Priyanka Kumara. - : Auckland University of Technology, 2017 Abstract: This thesis presents a framework for sentiment analysis for morphologically rich languages. Sentiment analysis is the domain of analysing and extracting people’s emotions, feelings, expressions, attitudes and experiences expressed in texts especially, in the digital media, such as web blogs, customer reviews. The primary issue of applying the contemporary sentiment classification techniques for morphologically rich languages is the unavailability of lexical resources. That is these techniques are highly resourced intensive, and the required lexical resources are not freely available for such languages. In addition, the methods are weak in adapting to the linguistic complexities that are shown in morphologically rich languages. The thesis and the related publications represent the first ever attempt of sentiment analysis for the Sinhala language, which is said to be a highly morphologically rich language. The thesis proposed novel approaches for generating the lexical resources for sentiment classification using limited resources. The first approach examined the cross-linguistic sentiment lexicon generation by considering a sentiment lexicon for English and basic dictionary of the target morphological rich language. In the subsequent task, a sentiment lexicon was generated using the novel approach incorporating morphological features. These morphological features include affixes; prefixes and suffixes. Thirdly, a graph based method was proposed to compile a lexical resource for sentiment classification with polarity scores. The researcher investigated the classical text classification techniques for Sinhala. The thesis identified the best classification algorithm for Sinhala with dominant linguistic features. Finally, an extensive set of experiments that demonstrated the exploration of language-specific classification features for Sinhala. These language-specific features include part of speech, negation, intensifiers and shifters. We introduce and discuss rule-based approaches to incorporate negations and intensifiers. The research contributes to sentiment classification for morphologically rich languages by proposing the framework that uses limited resources to build the lexical resources and efficient algorithms to classify opinions. The achievements confirm, concerning classification accuracies, the feasibility of sentiment classification for morphologically rich languages such as Sinhala. In addition, the achieved accuracies would be benchmarks for sentiment classification for Sinhala as well as other morphologically rich languages. Based on the promising outcomes and the simplicity, the proposed framework can be applied to any morphologically rich language. Keyword: Machine Learning; Natural Language Processing; Opinion Mining; Sentiment Analysis URL: http://hdl.handle.net/10292/10544 BASE Hide details 985 Comparison and Fine-grained Analysis of Sequence Encoders for Natural Language Processing Keller, Thomas Anderson. - : eScholarship, University of California, 2017 In: Keller, Thomas Anderson. (2017). Comparison and Fine-grained Analysis of Sequence Encoders for Natural Language Processing. UC San Diego: Computer Science. Retrieved from: http://www.escholarship.org/uc/item/0wg0r7hn (2017) BASE Show details 986 Information Extraction for the Seed Development Regulatory Networks of Arabidopsis Thaliana. ; Extraction d’Information pour les réseaux de régulation de la graine chez Arabidopsis Thaliana. Valsamou, Dialekti. - : HAL CCSD, 2017 In: https://tel.archives-ouvertes.fr/tel-01613508 ; Computation and Language [cs.CL]. Université Paris Saclay (COmUE), 2017. English. ⟨NNT : 2017SACLS027⟩ (2017) BASE Show details 987 Multilingual cyberbullying detection system: Detecting cyberbullying in Arabic content Haidar, Batoul; Chamoun, Maroun; Serhrouchni, Ahmed In: 2017 1st Cyber Security in Networking Conference (CSNet) ; https://hal.telecom-paris.fr/hal-03295349 ; 2017 1st Cyber Security in Networking Conference (CSNet), Oct 2017, Rio de Janeiro, Brazil. pp.1-8, ⟨10.1109/CSNET.2017.8242005⟩ (2017) BASE Show details 988 Research data supporting "Vancouver Welcomes You! Minimalist Location Metonymy Resolution" ... Gritta, Milan; Collier, Nigel; Limsopatham, N. - : Apollo - University of Cambridge Repository, 2017 BASE Show details 989 Dataset: tweets and analyses related to the paper 'The (Un)Predictability of Emotional Hashtags in Twitter' ... Kunneman, F.A.; Liebrecht, C.C.; Bosch, A.P.J. Van Den. - : Data Archiving and Networked Services (DANS), 2017 BASE Show details 990 Data: Timely identification of event start dates from Twitter ... Kunneman, F.A.; Hürriyetoğlu, A.; Oostdijk, N.H.J.. - : Data Archiving and Networked Services (DANS), 2017 BASE Show details 991 From Characters to Understanding Natural Language (C2NLU): Robust End-to-End Deep Learning for NLP (Dagstuhl Seminar 17042) Cho, Kyunghyun; Dyer, Chris; Blunsom, Phil. - : Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, 2017. : Dagstuhl Reports. Dagstuhl Reports, Volume 7, Issue 1, 2017 BASE Show details 992 The Evaluation of Ensemble Sentiment Classification Approach on Airline Services Using Twitter Wang, Zechen. - : Technological University Dublin, 2017 In: Dissertations (2017) BASE Show details 993 Training IBM Watson Using Automatically Generated Question-Answer Pairs Lee, Jangho; Kim, Gyuwan; Yoo, Jaeyoon. - 2017 BASE Show details 994 Stance Detection and Analysis in Social Media Sobhani, Parinaz. - : Université d'Ottawa / University of Ottawa, 2017 BASE Show details 995 Commonsense Knowledge for 3D Modeling: A Machine Learning Approach Hassani, Kaveh. - : Université d'Ottawa / University of Ottawa, 2017 BASE Show details 996 Compositional Lexical Semantics In Natural Language Inference Pavlick, Ellie In: Publicly Accessible Penn Dissertations (2017) BASE Show details 997 Laff-O-Tron: Laugh Prediction in TED Talks Acosta, Andrew D In: Master's Theses (2016) BASE Show details 998 Understanding Social Media Texts with Minimum Human Effort on #Twitter Tian, Tian; Tellier, Isabelle; Dinarelli, Marco... In: Language and the new (instant) media (PLIN) ; https://hal.archives-ouvertes.fr/hal-01490018 ; Language and the new (instant) media (PLIN), May 2016, Louvain-la-Neuve, Belgium (2016) BASE Show details 999 Structured Approaches for Exploring Interpersonal Relationships in Natural Language Text ... Chaturvedi, Snigdha. - : Digital Repository at the University of Maryland, 2016 BASE Show details 1000 How sick are you?Methods for extracting textual evidence to expedite clinical trial screening Shivade, Chaitanya P In: http://rave.ohiolink.edu/etdc/view?acc_num=osu1462810822 (2016) BASE Show details Page: 1...46 47 48 49 50 51 52 53 54

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