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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 BASE Show 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 Abstract: Computational approaches to opinion mining have mostly focused on polarity detection of product reviews by classifying the given text as positive, negative or neutral. While, there is less effort in the direction of socio-political opinion mining to determine favorability towards given targets of interest, particularly for social media data like news comments and tweets. In this research, we explore the task of automatically determining from the text whether the author of the text is in favor of, against, or neutral towards a proposition or target. The target may be a person, an organization, a government policy, a movement, a product, etc. Moreover, we are interested in detecting the reasons behind authors’ positions. This thesis is organized into three main parts: the first part on Twitter stance detection and interaction of stance and sentiment labels, the second part on detecting stance and the reasons behind it in online news comments, and the third part on multi-target stance classification. One may express favor (or disfavor) towards a target by using positive or negative language. Here, for the first time, we present a dataset of tweets annotated for whether the tweeter is in favor of or against pre-chosen targets, as well as for sentiment. These targets may or may not be referred to in the tweets, and they may or may not be the target of opinion in the tweets. We develop a simple stance detection system that outperforms all 19 teams that participated in a recent shared task competition on the same dataset (SemEval-2016 Task #6). Additionally, access to both stance and sentiment annotations allows us to conduct several experiments to tease out their interactions. Next, we proposed a novel framework for joint learning of stance and reasons behind it. This framework relies on topic modeling. Unlike other machine learning approaches for argument tagging which often require a large set of labeled data, our approach is minimally supervised. The extracted arguments are subsequently employed for stance classification. Furthermore, we create and make available the first dataset of online news comments manually annotated for stance and arguments. Experiments on this dataset demonstrate the benefits of using topic modeling, particularly Non-Negative Matrix Factorization, for argument detection. Previous models for stance classification often treat each target independently, ignoring the potential (sometimes very strong) dependency that could exist among targets. However, in many applications, there exist natural dependencies among targets. In this research, we relieve such independence assumptions in order to jointly model the stance expressed towards multiple targets. We present a new dataset that we built for this task and make it publicly available. Next, we show that an attention-based encoder-decoder framework is very effective for this problem, outperforming several alternatives that jointly learn dependent subjectivity through cascading classification or multi-task learning. Keyword: Deep Learning; Natural Language Processing; Social Media Analysis; Stance Detection; Text Classification URL: https://doi.org/10.20381/ruor-20460 http://hdl.handle.net/10393/36180 BASE Hide 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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