Distant-supervised Heterogeneous multitask learning for social - PowerPoint PPT Presentation

Oct 22, 2022 •42 likes •152 views

Distant-supervised Heterogeneous multitask learning for social event forecasting with multilingual indicators Liang Zhao George Mason University What are Spatiotemporal Events? Week 47 Week 46 Week 45 Epidemics outbreak on Week 47 ending

Distant-supervised Heterogeneous multitask learning for social event forecasting with multilingual indicators Liang Zhao George Mason University
What are Spatiotemporal Events? Week 47 Week 46 Week 45 Epidemics outbreak on Week 47 ending Nov 22, 2014 in southern region influenza Protests Civil unrest events on Mar 17, 2013 in Brazil
Open Source Indicators as the Social Sensor Protests on July 25, 2012, Mexico … … Tweet volume less than 10 Tweet volume larger than 10 A civil unrest event reported after July 25
Open Source Indicators as the Social Sensor 2013-14 Influenza Season Week 46 CDC flu activity map Flu tweets geographical distribution (reported on Week 47) (reported on Week 46) 1256 Flu tweets …
Challenge 1: Multilingual features 1. Must consider multilingual, because Moreover.. Countries with hundreds of languages • omit a group of people • Omit a language Cannot omit, even small ones • Social events can be triggered by any people 2. Too large dimension, too sparse feature vector Imagine a feature vector of a tweet: 10 nonzeros with 1M zeros.. 3. Few data for small language
Challenge 2: Cross-lingual semantic correlation 1. Features are highly semantically redundant One feature (https://www.profluentplus.com/blog/) 2. Features are correlated via multi-partite relationship (http://www.writeopinions.com/complete-multipartite-graph)
Challenge 3: Lack of language-wise supervision Zika outbreaks in Brazil No label on how much each group of language-speakers contribute (http://blogs.discovermagazine.com/science-sushi/2016/01/31/genetically- modified-mosquitoes-didnt-start-zika-ourbreak/) (http://www.foxnews.com/world/2017/12/18/mass-occupation- underscores-brazils-poverty-creates-angst.html)
Heterogeneous Multitask learning under distant supervision Task 1 (Language 1) Task 2 (Language 2) } Distant supervision Shared sparsity pattern Task 3 (Language 3) Word features Latent topics
Objective function Distant supervision: any language triggers, the whole triggers none language triggers, the whole not triggers Higher-level topic representation and transition matrix Shared sparsity patterns of Orthogonal constraint latent topics in different tasks Upper-bounded generalization error:
Optimization Equivalent problem: Alternating Direction Methods of Multipliers (ADMM) Solve Q: Dynamic programming Solve Θ and U : non-monotone spectral projected gradient descent Solve Z : second-order methods
Experiments

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