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MULTI-MODAL RETRIEVAL IN NEWS FEED APP USING GCDL TECHNIQUE
2017
International Journal of Recent Trends in Engineering and Research
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Existing methods proposed to use Canonical Correlation Analysis (CCA), manifolds learning, dual-wing harmoniums, deep autoencoder, and deep Boltzmann machine to approach the task. ...
Hashing methods have proven to be useful for a variety of tasks and have attracted extensive attention in recent years. ...
In short text segments (e.g., microblogs, captions, and tags), the similarities between words are especially important for retrieval. For example: journey versus travel, coast versus shore. ...
doi:10.23883/ijrter.2017.3365.aeikk
fatcat:6dmfmfsmtbaejale6t63ts7may
Mobile Social Multimedia Analytics in the Big Data Era
2017
ACM Transactions on Intelligent Systems and Technology
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We also thank Editor-in-Chief Professor Huan Liu for providing guidance. ...
ACKNOWLEDGMENTS We thank all the reviewers for their valuable comments that have ensured the high quality of this special issue and all the contributing authors for their interesting and innovative work ...
However, microblog event classification is challenging due to two facts: (i) its short/conversational nature and the incompatibility in meanings between the text and the corresponding image in social posts ...
doi:10.1145/3040934
fatcat:ikyx3dk4izb7xdkkzxiu72rxvm
Neural Information Retrieval: A Literature Review
[article]
2017
arXiv
pre-print
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While deep NNs have yet to achieve the same level of success in IR as seen in other areas, the recent surge of interest and work in NNs for IR suggest that this state of affairs may be quickly changing ...
In this work, we survey the current landscape of Neural IR research, paying special attention to the use of learned representations of queries and documents (i.e., neural embeddings). ...
Kunal Lad, Yang Liu, Amanda Lucio, Kristen Moor, Daniel Nelson, Geoffrey Potter, Harshal Priyadarshi, Vedhapriya Raman, Eric Roquemore, Juliette Seive, Abhishek Sinha, Ashwini Venkatesh, Yuxuan Wang, and ...
arXiv:1611.06792v3
fatcat:i2eqfj5l25epjcytgvifta4y4i
Research Trends on Deep Transformation Neural Models for Text Analysis in NLP Applications
2020
International journal of recent technology and engineering
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Applied extensive experiments were conducted on the deep learning model such as Recurrent Neural Network (RNN) / Long Short-Term Memory (LSTM) / Convolutional Neural Network (CNN) and Attentive Transformation ...
for text analysis. ...
Word prediction is useful for disabled persons and IQ of Autism children will be tested based on latent semantic analysis. word embedding, sentence embedding, and sequence-to-sequence modelling, learning ...
doi:10.35940/ijrte.b3838.079220
fatcat:6lv2flpkqfgq5b26owiwhm6rq4
#h00t: Censorship Resistant Microblogging
[article]
2011
arXiv
pre-print
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Naturally, with such short hashes, hashtags from different groups may collide and #h00t users will actually seek to create collisions. ...
#h00t presents an interface that is much like Twitter, except that hashtags are replaced with very short hashes (e.g., 24 bits) of the group identifier. ...
For example, one path to adoption would have Twitter or another microblogging service offer #h00t semantics over their entire existing service. ...
arXiv:1109.6874v1
fatcat:thwqvgfs2nfrzfxaikprsb74kq
Neural information retrieval: at the end of the early years
2017
Information retrieval (Boston)
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Because these modern NNs often comprise multiple interconnected layers, work in this area is often referred to as deep learning. ...
In this paper, Kezban Dilek Onal and Ye Zhang contributed equally. ...
We would also like to thank our anonymous reviewers for their constructive comments and the guest editors for their advice. ...
doi:10.1007/s10791-017-9321-y
fatcat:plrhhwkppjgb7l5r5daiyryj4q
The Emerging Trends of Multi-Label Learning
[article]
2020
arXiv
pre-print
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2020 pre-print arXiv:2011.11197v1 fatcat:zexkygo5tzf4pi7p2ig45kjdeuOther Versions
Besides these, there are tremendous efforts on how to harvest the strong learning capability of deep learning to better capture the label dependencies in multi-label learning, which is the key for deep ...
However, it is noted that there has been a lack of systemic studies that focus explicitly on analyzing the emerging trends and new challenges of multi-label learning in the era of big data. ...
For multi-label patent classification, which is regarded as multi-label text classification problem, [197] proposes a new deep learning model based on GCN to capture rich semantic information. ...
arXiv:2011.11197v2
fatcat:hu6w4vgnwbcqrinrdfytmmjbjm
Message from the general chair
2015
2015 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)
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To inject knowledge, we use a state-of-the-art system which cross-links (or "grounds") expressions in free text to Wikipedia. ...
Learning-based Multi-Sieve Co-reference Resolution with Knowledge Lev Ratinov and Dan Roth Saturday 11:00am-11:30am -202 A (ICC) We explore the interplay of knowledge and structure in co-reference resolution ...
and another based on deep analysis. ...
doi:10.1109/ispass.2015.7095776
dblp:conf/ispass/Lee15
fatcat:ehbed6nl6barfgs6pzwcvwxria
Deep Learning based Recommender System: A Survey and New Perspectives
[article]
2018
arXiv
pre-print
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Evidently, the field of deep learning in recommender system is flourishing. This article aims to provide a comprehensive review of recent research efforts on deep learning based recommender systems. ...
More concretely, we provide and devise a taxonomy of deep learning based recommendation models, along with providing a comprehensive summary of the state-of-the-art. ...
Deep Structured Semantic Model (DSSM) [65] is a deep neural network for learning semantic representations of entities in a common continuous semantic space and measuring their semantic similarities. ...
arXiv:1707.07435v6
fatcat:2q2dbfy2jvdydhbrmmbyrzctnq
A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances
[article]
2022
arXiv
pre-print
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2022 pre-print arXiv:2203.06935v1 fatcat:hzl5xptarfg53ncfus7jt4ezl4Other Versions
Instead of focusing on one specific field of affective analysis, we systematically review recent advances in the affective computing, and taxonomize unimodal affect recognition as well as multimodal affective ...
Affective computing is realized based on unimodal or multimodal data, primarily consisting of physical information (e.g., textual, audio, and visual data) and physiological signals (e.g., EEG and ECG signals ...
[372] proposed a deep dual recurrent neural network for encoding audio-text sequences which are then concatenated to predict the final emotion. ...
arXiv:2203.06935v3
fatcat:h4t3omkzjvcejn2kpvxns7n2qe
A Review of Deep Learning Research
2019
KSII Transactions on Internet and Information Systems
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processing, speech recognition and online advertising and so on. ...
With the advent of big data, deep learning technology has become an important research direction in the field of machine learning, which has been widely applied in the image processing, natural language ...
Acknowledgements We thank the anonymous referees for their helpful comments and suggestions on the initial version of this paper. ...
doi:10.3837/tiis.2019.04.001
fatcat:tefkvk3fvvanbkzwmjn44eoxsu
Sentiment Analysis on Social Network
2021
International Journal of Advanced Trends in Computer Science and Engineering
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In a time of social connectedness, individuals are turning out to be increasingly more excited about associating, sharing, and teaming up through informal communities, online networks, sites, Wikis, and ...
Lately, this aggregate insight has spread on various zones, with specific spotlight on fields identified with regular daily existence, for example, business, the travel industry, instruction, and wellbeing ...
•Large information models on behalf of semantic also wistfulness requests •Rule-based, adjusted also crossbreed method on behalf of semantic also estimation study •Deep knowledge method aimed at semantic ...
doi:10.30534/ijatcse/2021/531022021
fatcat:xxd25rb6qzhdvoceav52ynskke
Pretrained Transformers for Text Ranking: BERT and Beyond
2021
Proceedings of the 14th ACM International Conference on Web Search and Data Mining
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In the context of text ranking, these models produce high quality results across many domains, tasks, and settings. ...
The goal of text ranking is to generate an ordered list of texts retrieved from a corpus in response to a query for a particular task. ...
However, there remain many open research questions, and thus in addition to laying out the foundations of pretrained transformers for text ranking, this survey also attempts to prognosticate where the ...
doi:10.1145/3437963.3441667
fatcat:6teqmlndtrgfvk5mneq5l7ecvq
Pretrained Transformers for Text Ranking: BERT and Beyond
[article]
2021
arXiv
pre-print
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The goal of text ranking is to generate an ordered list of texts retrieved from a corpus in response to a query. ...
However, there remain many open research questions, and thus in addition to laying out the foundations of pretrained transformers for text ranking, this survey also attempts to prognosticate where the ...
Acknowledgements This research was supported in part by the Canada First Research Excellence Fund and the Natural Sciences and Engineering Research Council (NSERC) of Canada. ...
arXiv:2010.06467v3
fatcat:obla6reejzemvlqhvgvj77fgoy
Image Steganography Using HBC and RDH Technique
2014
International Journal of Computer Applications Technology and Research
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With these methods the performance of the stegnographic technique is improved in terms of PSNR value. ...
There are algorithms in existence for hiding data within an image. The proposed scheme treats the image as a whole. ...
in two types of text data: CNN news and microblogs from Twitter. ...
doi:10.7753/ijcatr0303.1001
fatcat:4i6tujs4oje2tnxf5c25eh26x4
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