Proceedings ArticleDOI
Location extraction from disaster-related microblogs
John Lingad,Sarvnaz Karimi,Jie Yin +2 more
- pp 1017-1020
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TLDR
This work investigates the feasibility of applying Named Entity Recognizers to extract locations from microblogs, at the level of both geo-location and point-of-interest, and shows that such tools once retrained on microblog data have great potential to detect the where information, even at the granularity of point- of-interest.Citations
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Journal ArticleDOI
Processing Social Media Messages in Mass Emergency: A Survey
TL;DR: This survey surveys the state of the art regarding computational methods to process social media messages and highlights both their contributions and shortcomings, and methodically examines a series of key subproblems ranging from the detection of events to the creation of actionable and useful summaries.
Journal ArticleDOI
Using Social Media to Enhance Emergency Situation Awareness
TL;DR: In this paper, a system uses natural language processing and data mining techniques to extract situation awareness information from Twitter messages generated during various disasters and crises, such as hurricanes, floods, and floods.
Journal ArticleDOI
A Survey of Location Prediction on Twitter
Xin Zheng,Jialong Han,Aixin Sun +2 more
TL;DR: A survey of location prediction on Twitter can be found in this article, where the authors focus on the prediction of user home locations, tweet locations, and mentioned locations, by summarizing Twitter network, tweet content, and tweet context.
Journal ArticleDOI
Social media analytics for natural disaster management
Zheye Wang,Xinyue Ye +1 more
TL;DR: A schema is proposed to categorize the gathered articles into 15 classes and facilitate the generation of data analysis tasks and suggest research opportunities and challenges in fusing social media data with authoritative datasets, i.e. census data and remote-sensing data.
Journal ArticleDOI
Hyper-resolution monitoring of urban flooding with social media and crowdsourcing data.
TL;DR: Social media and crowdsourcing data are employed to address hyper-resolution datasets for urban flooding and it is found these big data based flood monitoring approaches can complement the existing means of flood data collection.
References
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Proceedings Article
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
TL;DR: This work presents iterative parameter estimation algorithms for conditional random fields and compares the performance of the resulting models to HMMs and MEMMs on synthetic and natural-language data.
Proceedings ArticleDOI
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Proceedings ArticleDOI
Labeled LDA: A supervised topic model for credit attribution in multi-labeled corpora
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Proceedings Article
Named Entity Recognition in Tweets: An Experimental Study
TL;DR: The novel T-ner system doubles F1 score compared with the Stanford NER system, and leverages the redundancy inherent in tweets to achieve this performance, using LabeledLDA to exploit Freebase dictionaries as a source of distant supervision.