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Unifying Text, Metadata, and User Network Representations with a Neural Network for Geolocation Prediction


We propose a novel geolocation prediction model using a complex neural network.Our model unifies text, metadata, and user network representations with an attention mechanism to overcome previous ensemble approaches. In an evaluation using two open datasets, the proposed model exhibited a maximum 3.8%increase in accuracy and a maximum of 6.6%increase in accuracy against previous models. We?further analyzed several intermediate layers of our model, which revealed that their states capture some statistical characteristics of the datasets



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# Title Author Topic Medium Score
1 Highlights of EMNLP 2017: Exciting Datasets, Return of the Clusters, and More! Sebastian Ruder 641 resource 83.32
2 Diversity and network coherence as indicators of interdisciplinarity: case studies in bionanoscience Ismael Rafols, Martin Meyer 999 paper 78.53
3 NLP’s generalization problem, and how researchers are tackling it Ana Marasovic 711 resource 77.80
4 Neural information retrieval: at the end of the early years Kezban Dilek Onal, Ye Zhang, Ismail Sengor Altingovde, Md Mustafizur Rahman, Pinar Karagoz, Alex Braylan, Brandon Dang, Heng-Lu Chang, Henna Kim, Quinten McNamara, Aaron Angert, Edward Banner, Vivek K 232 survey 77.03
5 Introduction to Neural Machine Translation with GPUs (part 3) Kyunghyun Cho 753 tutorial 75.71
6 The data that transformed AI research—and possibly the world Dave Gershgorn 107 resource 73.87
7 The h’-Index, Effectively Improving the h-Index Based on the Citation Distribution Chun-Ting Zhang 999 paper 72.50
8 Codra: A Novel Discriminative Framework for Rhetorical Analysis Shafiq Joty, Giuseppe Carenini, Raymond T. Ng 999 paper 72.29
9 Similarity-driven Semantic Role Induction via Graph Partitioning Joel Lang, Mirella Lapata 999 paper 71.39
10 Do Altmetrics Work? Twitter and Ten Other Social Web Services Mike Thelwall, Stefanie Haustein, Vincent Larivière, Cassidy R. Sugimoto 999 paper 70.96
11 Recurrent Neural Network Tutorial, Part 4 – Implementing a GRU/LSTM RNN with Python and Theano Denny Britz 742 tutorial 70.37
12 DEEP LEARNING FOR CHATBOTS, PART 1 - INTRODUCTION Denny Britz 445 tutorial 69.83
13 Open Machine Learning Course. Topic 6. Feature Engineering and Feature Selection Arseny Kravchenko 711 resource 69.14
14 Recurrent Neural Networks Tutorial, Part 2 - Implementing a RNN with Python, Numpy, and Theano Denny Britz 741 tutorial 67.07
15 Deep Learning for NLP, advancements and trends in 2017 Javier 711 resource 66.12
16 Neural Machine Translation (seq2seq) Tutorial Thang Luong, Eugene Brevdo, Rui Zhao 753 tutorial 63.66
17 A large-scale community structure analysis in Facebook Emilio Ferrara 999 paper 63.48
18 Introduction to Visual Question Answering: Datasets, Approaches and Evaluation Javier Couto 411 resource 63.36
19 Recurrent Neural Networks Tutorial, Part 3- Backpropagation Through Time and Vanishing Gradients Denny Britz 741 tutorial 63.26
20 A Beginner's guide to Recurrent Networks and LSTMs Skymind 742 tutorial 62.69
21 How to handle Imbalanced Classification Problems in machine learning? Guest Blog 234 resource 62.63
22 Discriminative Syntax-based Word Ordering for Text Generation Yue Zhang, Stephen Clark 999 paper 62.49
23 Chatsbots with Machine Learning: Building Neural Conversational Agents Dmitry Persiyanov 999 resource 62.26
24 Sequence to sequence learning with Convolutional Neural networks Nuno Edgar Nunes Fernandes 744 tutorial 62.13
25 The 8 Neural Network Architectures Machine Learning Researchers Need to Learn James Le' 731 resource 61.86
26 Relevance of Unsupervised Metrics in Task-Oriented Dialogue for Evaluating Natural Language Generation Author Unknown 952 resource 61.62
27 RNNs in Tensorflow, a Practical Guide and Undocumented Features Denny Britz 741 tutorial 61.47
28 Recent Advances in Document Summarization Jin-ge Yao, Xiaojun Wan, Jianguo Xiao 421 survey 61.39
29 State-of-the-art neural coreference resolution for chatbots Thomas Wolf 756 tutorial 61.37
30 The 7 NLP Techniques That Will Change How You Communicate in the Future (Part II) James Le 133 resource 61.36
31 The 7 NLP Techniques That Will Change How You Communicate in the Future (Part I) James Le 112 resource 61.28
32 Deep Learning in NLP Vered Shwartz 711 resource 61.02
33 A survey of transfer learning Karl Weiss, Taghi M. Khoshgoftaar and DingDing Wang 978 resource 60.70
34 Graph Attention Networks Petar Veli?kovi?, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò and Yoshua Bengio 967 resource 60.63
35 NLP's ImageNet moment has arrived Sebastian Ruder 862 resource 60.53
36 An Overview of Multi-Task Learning in Deep Neural Networks Sebastian Ruder 829 tutorial 60.53
37 ICML+ACL’18: Structure Back in Play, Translation Wants More Context Andre Martins 956 resource 60.42
38 Lisbon Machine Learning Summer School Highlights Sebastian Ruder 107 resource 60.30
39 Deep Learning for NLP Best Practices Sebastian Ruder 713 tutorial 59.99
40 A social network's changing statistical properties and the quality of human innovation Brian Uzzi 999 paper 59.94
41 Recurrent Neural Networks Tutorial, Part 1 - Introduction to RNNs Denny Britz 741 tutorial 59.42
42 The 8 Neural Network Architectures Machine Learning Researchers Need to Learn Nand Kishor 711 resource 59.39
43 Open Machine Learning Course. Topic 5. Bagging and Random Forest Yury Kashnitskiy 711 resource 59.33
45 Negated bio-events: analysis and identification Raheel Nawaz, Paul Thompson, Sophia Ananiadou 999 paper 58.64
46 Learning Reinforcement Learning (with Code, Exercises and Solutions) Denny Britz 713 tutorial 58.36
47 Interpreting Machine Learning Models: An Overview Matthew Mayo, KDnuggets 711 resource 57.83
48 Natural Language Processing in Artificial Intelligence is almost human-level accurate. Worse yet, it gets smart! Rafal 133 tutorial 57.70
49 Transfer Learning in Natural Language Processing Prajjwal Bhargava 956 resource 57.53
50 Gimli: open source and high-performance biomedical name recognition David Campos, Sergio Matos, Jose Oliveira 999 paper 57.34
51 Deep Learning Achievements Over the Past Year Eduard Tyantov 711 resource 57.28
52 New approaches to Deep Networks:Capsules (Hinton), HTM (Numenta), Sparsey (Neurithmic Systems) and RCN (Vicarious) Gideon Kowadlo 731 resource 57.08
53 Word embeddings in 2017: Trends and future directions Sebastian Ruder 721 resource 56.97
54 Vector Representations of Words - TensorFlow Tutorials TensorFlow Team 721 tutorial 56.93
55 Four deep learning trends from ACL 2017: Part 2 Abigail See 713 resource 56.89
56 Introduction to Neural Machine Translation with GPUs (part 1) Kyunghyun Cho 753 tutorial 56.79
57 Tips on Building Neural Machine Translation Systems Graham Neubig 753 tutorial 56.69
58 Vector Representations of Words Author Unknown 731 resource 56.64
59 Recommendation in Industry Xavier Amatriain 999 tutorial 56.48
60 Ideas on interpreting machine learning Patrick Hall, Wen Phan, SriSatish Ambati 134 tutorial 56.27
61 K-Means & Other Clustering Algorithms: A Quick Intro with Python Nikos Koufos 571 tutorial 55.71
62 Rohan #2: Artificial intelligence, ?Progress/?Time Rohan Kapur 811 tutorial 55.52
63 Embed, encode, attend, predict: The new deep learning formula for state-of-the-art NLP models Matthew Honnibal 713 tutorial 55.14
64 Machine Learning for Humans Vishal Maini, Samer Sabri 134 tutorial 55.09
65 Deep Learning for NLP: An Overview of Recent Trends Elvis 711 resource 54.94
66 The Alignment Template Approach to Statistical Machine Translation Franz Josef Och, Hermann Ney 999 paper 54.82
67 Must Know Tips/Tricks in Deep Neural Networks Xiu-Shen Wei 713 tutorial 54.53
68 CNTK 204: Sequence to Sequence Networks with Text Data Microsoft 234 resource 54.51
69 An Overview of Proxy-label Approaches for Semi-supervised Learning Sebastian Ruder 581 resource 54.43
70 Multi-Way, Multilingual Neural Machine Translation with a Shared Attention Mechanism Orhan Firat, Kyunghyun Cho, Yoshua Bengio 999 paper 54.40
71 Comparison of Deepnet and Neuralnet Vincent Granville 711 resource 54.31
72 How to talk to your database Victor Zhong 731 resource 54.30
73 An index to quantify an individual’s scientific research output that takes into account the effect of multiple coauthorship J. E. Hirsch 999 paper 54.30