View Project


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



Suggested Topics

Partial Matches (at least half of words topic name appear in abstract)

Suggested Resources

Uses abstract to search the content of resources available in Topics. Sorted by relevance.

# Title Author Topic Medium Score
1 NLP’s generalization problem, and how researchers are tackling it Ana Marasovic 711 resource 103.73
2 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 713 resource 103.33
3 Codra: A Novel Discriminative Framework for Rhetorical Analysis Shafiq Joty, Giuseppe Carenini, Raymond T. Ng 999 paper 96.68
4 Highlights of EMNLP 2017: Exciting Datasets, Return of the Clusters, and More! Sebastian Ruder 641 resource 96.22
5 Diversity and network coherence as indicators of interdisciplinarity: case studies in bionanoscience Ismael Rafols, Martin Meyer 999 paper 95.13
6 Introduction to Neural Machine Translation with GPUs (part 3) Kyunghyun Cho 753 tutorial 93.19
7 Do Altmetrics Work? Twitter and Ten Other Social Web Services Mike Thelwall, Stefanie Haustein, Vincent Larivière, Cassidy R. Sugimoto 999 paper 92.17
8 Similarity-driven Semantic Role Induction via Graph Partitioning Joel Lang, Mirella Lapata 999 paper 92.10
9 The h’-Index, Effectively Improving the h-Index Based on the Citation Distribution Chun-Ting Zhang 999 paper 88.34
10 Discriminative Syntax-based Word Ordering for Text Generation Yue Zhang, Stephen Clark 999 paper 88.18
11 Open Machine Learning Course. Topic 6. Feature Engineering and Feature Selection Arseny Kravchenko 711 resource 85.47
12 Recent Advances in Document Summarization Jin-ge Yao, Xiaojun Wan, Jianguo Xiao 421 survey 85.21
13 The data that transformed AI research—and possibly the world Dave Gershgorn 107 resource 84.36
14 A social network's changing statistical properties and the quality of human innovation Brian Uzzi 999 paper 84.11
15 Deep Learning for NLP, advancements and trends in 2017 Javier 711 resource 82.34
16 Recurrent Neural Network Tutorial, Part 4 – Implementing a GRU/LSTM RNN with Python and Theano Denny Britz 742 tutorial 81.56
17 A large-scale community structure analysis in Facebook Emilio Ferrara 999 paper 81.55
18 DEEP LEARNING FOR CHATBOTS, PART 1 - INTRODUCTION Denny Britz 445 tutorial 79.29
19 How to handle Imbalanced Classification Problems in machine learning? Guest Blog 234 resource 79.08
20 The 8 Neural Network Architectures Machine Learning Researchers Need to Learn Nand Kishor 711 resource 78.11
21 A survey of transfer learning Karl Weiss, Taghi M. Khoshgoftaar and DingDing Wang 978 resource 78.02
22 Recurrent Neural Networks Tutorial, Part 2 - Implementing a RNN with Python, Numpy, and Theano Denny Britz 741 tutorial 77.60
23 Deep Learning in NLP Vered Shwartz 711 resource 77.18
24 The 8 Neural Network Architectures Machine Learning Researchers Need to Learn James Le' 731 resource 77.01
25 A Beginner's guide to Recurrent Networks and LSTMs Skymind 742 tutorial 76.15
26 The 7 NLP Techniques That Will Change How You Communicate in the Future (Part I) James Le 112 resource 75.90
27 Sequence to sequence learning with Convolutional Neural networks Nuno Edgar Nunes Fernandes 744 tutorial 75.65
28 NLP's ImageNet moment has arrived Sebastian Ruder 862 resource 74.86
29 Neural Machine Translation (seq2seq) Tutorial Thang Luong, Eugene Brevdo, Rui Zhao 753 tutorial 74.85
30 An Overview of Multi-Task Learning in Deep Neural Networks Sebastian Ruder 829 tutorial 74.31
31 Deep Learning for NLP Best Practices Sebastian Ruder 713 tutorial 74.25
32 The 7 NLP Techniques That Will Change How You Communicate in the Future (Part II) James Le 133 resource 74.00
33 Introduction to Visual Question Answering: Datasets, Approaches and Evaluation Javier Couto 411 resource 73.67
34 ICML+ACL’18: Structure Back in Play, Translation Wants More Context Andre Martins 956 resource 73.42
35 Negated bio-events: analysis and identification Raheel Nawaz, Paul Thompson, Sophia Ananiadou 999 paper 73.18
36 State-of-the-art neural coreference resolution for chatbots Thomas Wolf 756 tutorial 73.12
37 Machine Learning for Humans Vishal Maini, Samer Sabri 134 tutorial 72.92
38 Rohan #2: Artificial intelligence, ?Progress/?Time Rohan Kapur 811 tutorial 72.67
39 Chatsbots with Machine Learning: Building Neural Conversational Agents Dmitry Persiyanov 999 resource 72.54
40 Lisbon Machine Learning Summer School Highlights Sebastian Ruder 107 resource 72.37
41 Graph Attention Networks Petar Veli?kovi?, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò and Yoshua Bengio 967 resource 72.16
42 Clustering cliques for graph-based summarization of the biomedical research literature Han Zhang, Marcelo Fiszman, Dongwook Shin, Bartomiej Wilkowski, Thomas Rindflesch 999 paper 72.12
43 Recurrent Neural Networks Tutorial, Part 3- Backpropagation Through Time and Vanishing Gradients Denny Britz 741 tutorial 72.01
44 PyTorch-GAN Jun-Yan Zhu, Richard Zhang, Deepak Pathak, Trevor Darrell, Alexei A. Efros, Oliver Wang, Eli Shechtman 731 library 71.89
45 Vector Representations of Words Author Unknown 731 resource 71.59
46 Open Machine Learning Course. Topic 5. Bagging and Random Forest Yury Kashnitskiy 711 resource 70.15
47 Vector Representations of Words - TensorFlow Tutorials TensorFlow Team 721 tutorial 69.80
48 Gimli: open source and high-performance biomedical name recognition David Campos, Sergio Matos, Jose Oliveira 999 paper 69.67
49 RNNs in Tensorflow, a Practical Guide and Undocumented Features Denny Britz 741 tutorial 69.65
50 How do we capture structure in relational data? Matthew Das Sarma 711 resource 69.64
51 Comparison of Deepnet and Neuralnet Vincent Granville 711 resource 69.60
52 Literature Mining for the Discovery of Hidden Connections between Drugs, Genes and Diseases Frijters, Raoul AND van Vugt, Marianne AND Smeets, Ruben AND van Schaik, René AND de Vlieg, Jacob AND Alkema, Wynand 999 paper 69.59
53 Relevance of Unsupervised Metrics in Task-Oriented Dialogue for Evaluating Natural Language Generation Author Unknown 952 resource 69.30
54 Word embeddings in 2017: Trends and future directions Sebastian Ruder 721 resource 69.25
55 Lexicalization and Generative Power in Ccg Marco Kuhlmann, Alexander Koller, Giorgio Satta 999 paper 69.04
56 Four deep learning trends from ACL 2017: Part 2 Abigail See 713 resource 69.02
57 Deep Learning Achievements Over the Past Year Eduard Tyantov 711 resource 68.76
58 New approaches to Deep Networks:Capsules (Hinton), HTM (Numenta), Sparsey (Neurithmic Systems) and RCN (Vicarious) Gideon Kowadlo 731 resource 68.63
59 A general framework for analysing diversity in science, technology and society Andy Stirling 999 paper 68.55
60 Tips on Building Neural Machine Translation Systems Graham Neubig 753 tutorial 68.48
61 Introduction to Neural Machine Translation with GPUs (part 1) Kyunghyun Cho 753 tutorial 68.39
63 Transfer Learning in Natural Language Processing Prajjwal Bhargava 956 resource 68.20
64 Natural Language Processing in Artificial Intelligence is almost human-level accurate. Worse yet, it gets smart! Rafal 133 tutorial 67.78
65 SippyCup Unit 2: Travel queries Bill MacCartney 365 tutorial 67.77
66 Recurrent Neural Networks Tutorial, Part 1 - Introduction to RNNs Denny Britz 741 tutorial 67.64
67 Recommendation in Industry Xavier Amatriain 999 tutorial 67.23
68 Bayesian Statistics explained to Beginners in Simple English NSS 102 tutorial 67.03
69 Recurrent Neural Networks Stephen Grossberg 741 paper 66.95
70 A survey of cross-lingual embedding models Sebastian Ruder 721 tutorial 66.72
71 iPython Noetbook for Translation with a Sequence to Sequence Network and Attention Tutorial Sean Robertson 753 library 66.35
72 Interpreting Machine Learning Models: An Overview Matthew Mayo, KDnuggets 711 resource 66.20
73 Transfer Learning - Machine Learnings Next Frontier Sebastian Ruder 978 tutorial 66.16
74 On word embeddings - Part 2: Approximating the Softmax Sebastian Ruder 721 tutorial 65.86
75 Must Know Tips/Tricks in Deep Neural Networks Xiu-Shen Wei 713 tutorial 65.44
76 Ideas on interpreting machine learning Patrick Hall, Wen Phan, SriSatish Ambati 134 tutorial 65.43
77 Vector Representation of Words TensorFlow 321 tutorial 65.38
78 Open Machine Learning Course. Topic 3. Classification, Decision Trees and k Nearest Neighbors Yury Kashnitskiy 711 resource 65.19
79 Learning Reinforcement Learning (with Code, Exercises and Solutions) Denny Britz 713 tutorial 65.05
80 An Overview of Proxy-label Approaches for Semi-supervised Learning Sebastian Ruder 581 resource 64.89
81 Multi-Task Learning Objectives for Natural Language Processing Author Unknown 133 resource 64.77
82 The e-Index, Complementing the h-Index for Excess Citations Chun-Ting Zhang 999 paper 64.77
83 Text Summarization using Sequence-to-Sequence model in Tensorflow and GPU computing: Part I – How to get things running cyberyu 132 resource 64.73
84 Train Neural Machine Translation Models with Sockeye Felix Hieber, Tobias Domhan 753 tutorial 64.68
85 Deep Learning 2: Part 1 Lesson 4 Hiromi Suenaga 711 resource 64.66
86 Deep Learning for NLP: An Overview of Recent Trends Elvis 711 resource 64.64
87 Machine Learning Glossary Author Unknown 107 resource 64.62
88 What is machine learning? Everything you need to know Nick Heath 711 resource 64.37
89 A miscellany of fun deep learning papers Adrian Colyer 711 resource 63.97
90 A Beginner’s Guide to Deep Reinforcement Learning Adam Gibson, Chris Nicholson, Josh Patterson 857 library 63.92
91 Recursive Neural Networks with PyTorch James Bradbury 743 tutorial 63.87
92 K-Means & Other Clustering Algorithms: A Quick Intro with Python Nikos Koufos 571 tutorial 63.76