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Past, present, and future of social TV: A categorization

Pablo Cesar, David Geerts
2011 2011 IEEE Consumer Communications and Networking Conference (CCNC)  
Current offerings can be categorized based on the social purpose: content selection and recommendation, communication, community building, and status update.  ...  Media software like Boxee allows users to recommend and share favorite television programs, and friends can jointly watch television remotely as demonstrated by Motorola's Social TV.  ...  The next section overviews in a structured manner past and present efforts regarding social TV.  ... 
doi:10.1109/ccnc.2011.5766487 dblp:conf/ccnc/CesarG11 fatcat:eziijuujmbdhpffperinfy42sa

Neural Text Generation: Past, Present and Beyond [article]

Sidi Lu, Yaoming Zhu, Weinan Zhang, Jun Wang, Yong Yu
2018 arXiv   pre-print
This paper presents a systematic survey on recent development of neural text generation models.  ...  We thus introduce the recently proposed methods for text generation based on reinforcement learning, re-parametrization tricks and generative adversarial nets (GAN) techniques.  ...  Conclusion This paper presents an overview of the classic and recently proposed neural text generation models.  ... 
arXiv:1803.07133v1 fatcat:brfobli56nf6pn3kzhno5dwnea

Automatic Text Summarization: Past, Present and Future [chapter]

Horacio Saggion, Thierry Poibeau
2012 Multi-source, Multilingual Information Extraction and Summarization  
2004 -TEXT SUMMARIZATION 36 Alignment Presents a model and gives an overview of related research.  ...  Use of document structure top-down strategy + superficial features Cut-and-paste HORACIO SAGGION IBERAMIA 2004 -TEXT SUMMARIZATION 30 Liddy'91 Professional abstractors from ERIC and PsycINFO  ...  Search using the following formula (note the use logarithm) Viterbi algorithm can be used to find the best sequence Headline generation HORACIO SAGGION IBERAMIA 2004 -TEXT SUMMARIZATION 133 One has to  ... 
doi:10.1007/978-3-642-28569-1_1 dblp:series/tanlp/SaggionP13 fatcat:wwhklog67jd2heqb2aw727wfbe

Neural Text Categorizer for Exclusive Text Categorization

Tae-Ho Jo
2008 Journal of Information Processing Systems  
Since the proposed neural network is intended originally only for text categorization, it is called NTC (Neural Text Categorizer) in this research.  ...  Numerical vectors representing documents for tasks of text mining have inherently two main problems: huge dimensionality and sparse distribution.  ...  In 2000, Cristianini and Shawe-Taylor presented a case of applying SVM to text categorization in their textbook [2] .  ... 
doi:10.3745/jips.2008.4.2.077 fatcat:chrnteuwyzaafnf7m2tmwerew4

Text and Hypertext Categorization [chapter]

Houda Benbrahim, Max Bramer
2009 Lecture Notes in Computer Science  
This chapter surveys the state of the art in text categorization and hypertext categorization, focussing particularly on issues of representation that differentiate them from 'conventional' classification  ...  These have features that add further complexity to the categorization task but also offer the possibility of using information that is not available in standard text classification, such as metadata and  ...  There is an important difference between Hypertext Categorization and Text Categorization at the tokenization stage.  ... 
doi:10.1007/978-3-642-03226-4_2 fatcat:h24vwhiaujhmdcnw7pi4jxlwla

Family portraits: past and present representations of parents in special education text books

Dianne L. Ferguson, Philip M. Ferguson, Joanne Kim, Corrine Li
2013 International Journal of Inclusive Education  
These text books are typically used in preservice teacher education courses as surveys of the education of "exceptional children."  ...  The paper compares and contrasts how the representations of families by leading scholars in special education have changed over time.  ...  One outcome of this new study has been to evaluate how well these previous themes work in summarizing the content of both past and present textbooks.  ... 
doi:10.1080/13603116.2013.826293 fatcat:h5w35pubabbfnd4ddxmv3fbf64

Combining classifiers in text categorization

Leah S. Larkey, W. Bruce Croft
1996 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '96  
Three different types of classifiers were investigatedin the context of a text categorization problem in the medical domain: the automatic assignment of ICD9 codes to dictated inpatient discharge summaries  ...  For this specific medical categorization problem, new query formulation and weighting methods used in the k-nearest-neighbor classifier improved performance.  ...  Acknowledgments We would like to thank David Aronow for his help in categorizing the section titles in the documents and David Fisher, Fang-Fang Feng, and Stephen Soderland for the NLP tagging.  ... 
doi:10.1145/243199.243276 dblp:conf/sigir/LarkeyC96 fatcat:jypl2brxjjgazjlrv6r5rclmli

Neural Discourse Structure for Text Categorization [article]

Yangfeng Ji, Noah Smith
2017 arXiv   pre-print
We show that discourse structure, as defined by Rhetorical Structure Theory and provided by an existing discourse parser, benefits text categorization.  ...  Our approach uses a recursive neural network and a newly proposed attention mechanism to compute a representation of the text that focuses on salient content, from the perspective of both RST and the task  ...  We thank Dallas Card and Jesse Dodge for helping prepare the Media Frames Corpus and the Congressional bill corpus. This work was made possible by a University of Washington Innovation Award.  ... 
arXiv:1702.01829v2 fatcat:ygfhyhpuknfjhnntmpuryiwfwy

Sequential patterns for text categorization

S. Jaillet, A. Laurent, M. Teisseire
2006 Intelligent Data Analysis  
Text categorization is a well-known task based essentially on statistical approaches using neural networks, Support Vector Machines and other machine learning algorithms.  ...  We propose, in this paper, to extend this approach by using sequential patterns in the SPaC method (Sequential Patterns for Classification) for text categorization.  ...  Textual representation and categorization Text categorization is the task of assigning a boolean value to each pair (document, category).  ... 
doi:10.3233/ida-2006-10302 fatcat:e74x4w3xgjdhdhc5ptaqngq5oi

Neural Discourse Structure for Text Categorization

Yangfeng Ji, Noah A. Smith
2017 Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)  
We show that discourse structure, as defined by Rhetorical Structure Theory and provided by an existing discourse parser, benefits text categorization.  ...  Our approach uses a recursive neural network and a newly proposed attention mechanism to compute a representation of the text that focuses on salient content, from the perspective of both RST and the task  ...  We thank Dallas Card and Jesse Dodge for helping prepare the Media Frames Corpus and the Congressional bill corpus. This work was made possible by a University of Washington Innovation Award.  ... 
doi:10.18653/v1/p17-1092 dblp:conf/acl/JiS17 fatcat:kji5xpzccfaffkcsphzybbzqim

Networking Past and Present

R.I.M. Dunbar, Nicolas Baumard, Marcus J. Hamilton, Paul Hooper, Daniel N. Finkel, Herbert Gintis
2012 Cliodynamics  
Santa Fe Institute and Central European University e-mail: hgintis@comcast.net In his engaging Social Evolution Forum contribution, Networking Past and Present, R.I.M.  ...  Finkel: Social Cognition in a Digital World University of Connecticut e-mail: daniel.finkel@uconn.edu In his excellent target article, Networking Past and Present, Dunbar argues that though contemporary  ... 
doi:10.21237/c7clio3215774 fatcat:fti7qxmaabbfle6dbaqhnrsa4y

Text categorization by fuzzy domain adaptation

Vahid Behbood, Jie Lu, Guangquan Zhang
2013 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)  
As an example of text categorization, 20Newsgroup data set is used in the experiments to validate the proposed method.  ...  Machine learning methods have attracted attention of researches in computational fields such as classification/categorization.  ...  ACKNOWLEDGMENT The work presented in this paper was supported by Australian Research Council (ARC) under the Discovery Projects DP088739 and DP110103733.  ... 
doi:10.1109/fuzz-ieee.2013.6622530 dblp:conf/fuzzIEEE/BehboodLZ13 fatcat:5qbgu4azy5ctlcxhy2knavcr4q

Learning Timeline Difference for Text Categorization

Fumiyo Fukumoto, Yoshimi Suzuki
2015 Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing  
This paper addresses text categorization problem that training data may derive from a different time period from the test data.  ...  We present a learning framework which extends a boosting technique to learn accurate model for timeline adaptation.  ...  presented an approach to classify documents in scenarios where the method uses information about both the past and the future, and this information may change over time (Salles et al., 2010) .  ... 
doi:10.18653/v1/d15-1093 dblp:conf/emnlp/FukumotoS15 fatcat:ndkgcrlyzbh7dkwy3avrlknt6y

Text Categorization Based on Topic Model

Shibin Zhou, Kan Li, Yushu Liu
2009 International Journal of Computational Intelligence Systems  
In general, experiments show LDACLM model is effective and outperform Naïve Bayes with Laplace smoothing and Rocchio algorithm but little inferior to SVM for text categorization.  ...  In this paper, we propose LDACLM or Latent Dirichlet Allocation Category Language Model for text categorization and estimate parameters of models by variational inference.  ...  Acknowledgement We would like to thank the anonymous reviewers for their valuable comments and suggestions. We are grateful for Zhao Cao's helpful discussion and advice.  ... 
doi:10.2991/ijcis.2009.2.4.8 fatcat:4qbmdhnwg5aofiq23m7qxltkum

Text Categorization Based on Topic Model

Shibin Zhou, Kan Li, Yushu Liu
2009 International Journal of Computational Intelligence Systems  
In general, experiments show LDACLM model is effective and outperform Naïve Bayes with Laplace smoothing and Rocchio algorithm but little inferior to SVM for text categorization.  ...  In this paper, we propose LDACLM or Latent Dirichlet Allocation Category Language Model for text categorization and estimate parameters of models by variational inference.  ...  Acknowledgement We would like to thank the anonymous reviewers for their valuable comments and suggestions. We are grateful for Zhao Cao's helpful discussion and advice.  ... 
doi:10.1080/18756891.2009.9727671 fatcat:3ia22mt2gzfrrfahfaqdcyk4pu
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