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Bilateral Correspondence Model for Words-and-Pictures Association in Multimedia-Rich Microblogs

Zhiyu Wang, Peng Cui, Lexing Xie, Wenwu Zhu, Yong Rui, Shiqiang Yang
2014 ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)  
We then propose a novel generative model, called the Bilateral Correspondence Latent Dirichlet Allocation (BC-LDA) model.  ...  The rich semantics in microblogs provide an opportunity to endow images with higherlevel semantics beyond object labels.  ...  In microblog, the tag variety is the same as the vocabulary of natural language.  ... 
doi:10.1145/2611388 fatcat:7dnjnthfbbdkdb6ruhhdo2b35u

Generating event storylines from microblogs

Chen Lin, Chun Lin, Jingxuan Li, Dingding Wang, Yang Chen, Tao Li
2012 Proceedings of the 21st ACM international conference on Information and knowledge management - CIKM '12  
We first propose a language model with dynamic pseudo relevance feedback to obtain relevant tweets, and then generate storylines via graph optimization.  ...  Microblogging service has emerged to be a dominant web medium for billions of individuals sharing and spreading instant news and information, therefore monitoring the event evolution on microblog sphere  ...  Latent Semantic Analysis (LSA): identifies semantically important sentences by conducting latent semantic analysis; 4.  ... 
doi:10.1145/2396761.2396787 dblp:conf/cikm/LinLLWCL12 fatcat:7w2rmpvdpbgevfe27um7i25yyu

Query Expansion for Microblog Retrieval Focusing on an Ensemble of Features

Abu Nowshed Chy, Md Zia Ullah, Masaki Aono
2019 Journal of Information Processing  
Upon retrieving tweets by our proposed topic modeling based query expansion, we utilize the pseudo-relevance feedback and a new temporal relatedness approach to select the candidate tweets.  ...  In microblog search, vocabulary mismatch is a persisting problem due to the brevity of tweets and frequent use of unconventional abbreviations.  ...  Retrieval Model We use the language model with Dirichlet smoothing [44] to retrieve the tweets.  ... 
doi:10.2197/ipsjjip.27.61 fatcat:n3q4l6tmn5fh7ppdjvqpbcmjt4

Second order probabilistic models for within-document novelty detection in academic articles

Laurence A.F. Park, Simeon Simoff
2014 Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval - SIGIR '14  
Retrieval Zhou Yu, Fei Wu, Yi Yang, Qi Tian, Jiebo Luo, Yueting Zhuang  Active Hashing with Joint Data Example and Tag Selection Qifan Wang, Luo Si, Zhiwei Zhang, Ning Zhang  Latent Semantic  ...  Using the Cross-Entropy Method to Re-Rank Search Results Haggai Roitman, Shay Hummel, Oren Kurland 9.  ... 
doi:10.1145/2600428.2609520 dblp:conf/sigir/ParkS14 fatcat:ye2rtri2xjbyrjvkkzgt7srcfu

Hot Topic Discovery across Social Networks Based on Improved LDA Model

2021 KSII Transactions on Internet and Information Systems  
data set, then obtains the potential topic distribution in the text through the improved LDA model.  ...  Finally, it adopts a heat evaluation method based on the word frequency of topic label words to take the latent topic with the highest heat value as a hot topic.  ...  Related works 2.1 Topic models Topic modeling techniques have been widely used in natural language processing (NLP) to discover latent semantic structures hidden in large-scale corpus. Deerwster et al  ... 
doi:10.3837/tiis.2021.11.004 fatcat:e2jx26wd7vavpkghybck4j5p6i

Learning Improved Semantic Representations with Tree-Structured LSTM for Hashtag Recommendation: An Experimental Study

Rui Zhu, Delu Yang, Yang Li
2019 Information  
Most existing work tries to use deep neural networks to learn microblog post representation based on the semantic combination of words.  ...  A hashtag is a type of metadata tag used on social networks, such as Twitter and other microblogging services.  ...  Latent Dirichlet Allocation (LDA) Following the same idea from [16] , we use the standard LDA model [17] for the task.  ... 
doi:10.3390/info10040127 fatcat:rnvvqlxrdrethlrwvo22kr7dq4

MULTI-MODAL RETRIEVAL IN NEWS FEED APP USING GCDL TECHNIQUE

2017 International Journal of Recent Trends in Engineering and Research  
For example, images can be used to find semantically relevant textual information.  ...  To demonstrate the effectiveness of the proposed method, we evaluate the proposed method on three commonly used cross-media data sets are used in this work.  ...  codes for different modalities of one instance through collective matrix factorization with latent factor model.  Semantic correlation maximization (SCM) integrates semantic labels into the hashing learning  ... 
doi:10.23883/ijrter.2017.3365.aeikk fatcat:6dmfmfsmtbaejale6t63ts7may

Self-Attentive Neural Network for Hashtag Recommendation

Delu Yang, College of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, China, Rui Zhu, Yang Li, College of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, China, College of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, China
2019 Journal of Engineering Science and Technology Review  
To capture the interactive information between words and understand the content of microblog posts deeply, this study proposed a neural network model based on a word-level self-attention mechanism.  ...  The effectiveness of the proposed model was verified through experiments of large-scale datasets.  ...  Model Training The proposed model is trained in a supervised manner by minimizing the cross-entropy error of the hashtag classification.  ... 
doi:10.25103/jestr.122.15 fatcat:cwcy3gjo3bgo5czf52l2cpr6ea

Topical Co-Attention Networks for hashtag recommendation on microblogs

Yang Li, Ting Liu, Jingwen Hu, Jing Jiang
2019 Neurocomputing  
Motivated by the successful use of neural models for many natural language processing tasks, in this paper, we adopt an attention based neural network to learn the representation of a microblog post.  ...  Unlike previous works, which only focus on content attention of microblogs, we propose a novel Topical Co-Attention Network (TCAN) that jointly models content attention and topic attention simultaneously  ...  [10] try to integrate latent topical information into translation model.  ... 
doi:10.1016/j.neucom.2018.11.057 fatcat:aymz7nsnpzaqdahucb7mw6s4d4

Utilizing Microblogs for Automatic News Highlights Extraction [chapter]

Zhongyu Wei, Wei Gao
2017 Social Media Content Analysis  
We propose a novel method to improve news highlights extraction by using microblogs.  ...  The hypothesis is that microblog posts, although noisy, are not only indicative of important pieces of information in the news story, but also inherently "short and sweet" resulting from the artificial  ...  QualityLM measures writing quality of a tweet based on language model. We train uni-gram, bi-gram and tri-gram language models using maximum-likelihood estimation.  ... 
doi:10.1142/9789813223615_0019 fatcat:5iauff3eizh55dmtawwigq2bku

Text Analytics in Social Media [chapter]

Xia Hu, Huan Liu
2012 Mining Text Data  
We next discuss the research progress of applying text analytics in social media from different perspectives, and show how to improve existing approaches to text representation in social media, using real-world  ...  For the query likelihood, the authors propose several approaches to estimate the query language model and the news headline language model.  ...  Then we extract semantic concepts from the retrieved Wikipedia pages.  ... 
doi:10.1007/978-1-4614-3223-4_12 fatcat:ynmfabrhpjf6vils663o3rs2za

Time-Aware Rank Aggregation for Microblog Search

Shangsong Liang, Zhaochun Ren, Wouter Weerkamp, Edgar Meij, Maarten de Rijke
2014 Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management - CIKM '14  
We propose a rank aggregation method, TimeRA, that is able to infer the rank scores of documents via latent factor modeling.  ...  Our experimental results show that it significantly outperforms state-of-the-art rank aggregation and time-sensitive microblog search algorithms.  ...  Latent factor modeling Latent factor models are often used in collaborative filtering (CF) and recommender systems [8, 25] .  ... 
doi:10.1145/2661829.2661905 dblp:conf/cikm/LiangRWMR14 fatcat:m4gqnyzy4fcwblohgkugfaglta

Detection of topic on Health News in Twitter Data

Shum Chen Yau, Data Science Research Lab, School of Computing, UUM College of Arts and Sciences, Universiti Utara Malaysia, 06010 Sintok, Kedah, MALAYSIA, Juhaida Abu Bakar, Azian Azamimi Abdullah, Nor Hazlyna Harun, Ruziana Mohamad Rasli, Lim Zheng Yang, Evon Thum Yi Mun, Data Science Research Lab, School of Computing, UUM College of Arts and Sciences, Universiti Utara Malaysia, 06010 Sintok, Kedah, MALAYSIA, Medical Devices and Life Sciences Cluster, Sport Engineering Research Centre, Centre of Excellence (SERC), Universiti Malaysia Perlis (UniMAP), 02600 Arau, Perlis, MALAYSIA, Faculty of Electronic Engineering Technology, Universiti Malaysia Perlis (UniMAP), 02600 Arau, Perlis, MALAYSIA, Data Science Research Lab, School of Computing, UUM College of Arts and Sciences, Universiti Utara Malaysia, 06010 Sintok, Kedah, MALAYSIA (+3 others)
2021 Emerging Advances in Integrated Technology  
EEGOTM sport device were used to record the EMG signal from four types of muscles which are Erector Spinae, Latissimus Dorsi, Internal Oblique and External Oblique.  ...  Electromyography (EMG) signal is an analysis of electrical signals generated during muscular contractions that have been used to measure and record electrical muscle activity usually applied for medical  ...  Research by [3] described fuzzy latent semantic analysis (FLSA), a singular method in subject matter modelling that use fuzzy angle.  ... 
doi:10.30880/emait.2021.02.02.004 fatcat:ovca5yatrvcj5fd3zhjs3ofnl4

Wikipedia-based topic clustering for microblogs

Tan Xu, Douglas W. Oard
2011 Proceedings of the American Society for Information Science and Technology  
The main idea is to link terms in microblog posts to Wikipedia pages and then to leverage Wikipedia's link structure to estimate semantic similarity, Results show statistically significant relative improvements  ...  Linking terms in microblog posts to Wikipedia pages is also shown to offer a useful basis for cluster labeling.  ...  They first used Latent Dirichlet Allocation (LDA) to build "topic models" for each author based on all tweets posted by that author.  ... 
doi:10.1002/meet.2011.14504801186 fatcat:msclma4yirakfcbdwjwgwck5n4

VELDA: Relating an Image Tweet's Text and Images

Tao Chen, Hany SalahEldeen, Xiangnan He, Min-Yen Kan, Dongyuan Lu
2015 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
Experiments on real-world image tweets in both Englishand Chinese and other user generated content, show that VELDA significantly outperforms existingmethods on cross-modality image retrieval.  ...  Even in other domains where emotion does not factor in imagechoice directly, our VELDA model demonstrates good generalization ability, achieving higher fidelity modeling of such multimedia documents.  ...  We examine the image-text correlation and its modeling for cross-modality image retrieval, in both microblog posts as well as other image-text datasets.  ... 
doi:10.1609/aaai.v29i1.9168 fatcat:2p5tmwidazduthfzfzqypaxtcq
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