An Encoder-Memory-Decoder Framework for Sub-Event Detection in Social Media

Chen, Guandan; Xu, Nan; Mao, Weiji

doi:10.1145/3269206.3269256

Cited by 10 publications

(5 citation statements)

References 7 publications

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“…Related work outside of Crisis NLP can also be used to extract information from tweets, in the form of events. Chen et al (2018) use an encoderdecoder framework to extract sub-events from each tweet, while Rudra et al (2018) use noun-verb pairs to represent sub-events, where each pair is ranked based on their overlap score in tweets. Some approaches outside the crisis domain that focus on extracting textual sub-events from tweets or documents, in a sequence classification setup (Bekoulis et al, 2019).…”

Section: Information Extraction and Classification Inmentioning

confidence: 99%

A Novel Framework for Detecting Important Subevents from Crisis Events via Dynamic Semantic Graphs

Spiliopoulou¹,

Saha²,

Tetreault³

et al. 2021

Proceedings of the Seventh Workshop on Noisy User-Generated Text (W-Nut 2021)

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Social media is an essential tool to share information about crisis events, such as natural disasters. Event Detection aims at extracting information in the form of an event, but considers each event in isolation, without combining information across sentences or events. Many posts in Crisis NLP contain repetitive or complementary information which needs to be aggregated (e.g., the number of trapped people and their location) for disaster response. Although previous approaches in Crisis NLP aggregate information across posts, they only use shallow representations of the content (e.g., keywords), which cannot adequately represent the semantics of a crisis event and its sub-events. In this work, we propose a novel framework to extract critical subevents from a large-scale crisis event by combining important information across relevant tweets. Our framework first converts all the tweets from a crisis event into a temporallyordered set of graphs. Then it extracts subgraphs that represent semantic relationships connecting verbs and nouns in 3 to 6 node subgraphs. It does this by learning edge weights via Dynamic Graph Convolutional Networks (DGCNs) and extracting smaller, relevant subgraphs. Our experiments show that our extracted structures (1) are semantically meaningful sub-events and (2) contain information important for the large crisis-event. Furthermore, we show that our approach significantly outperforms event detection baselines, highlighting the importance of aggregating information across tweets for our task.

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Section: Information Extraction and Classification Inmentioning

confidence: 99%

A Novel Framework for Detecting Important Subevents from Crisis Events via Dynamic Semantic Graphs

Spiliopoulou¹,

Saha²,

Tetreault³

et al. 2021

Proceedings of the Seventh Workshop on Noisy User-Generated Text (W-Nut 2021)

View full text Add to dashboard Cite

show abstract

“…One aspect of crisis management is identifying sub-events as a significant crisis unfolds (Abhik and Toshniwal 2013). Studies have tried to detect sub-events from tweets using different methods, both supervised and unsupervised (Ardalan et al ;Chen, Xu, and Mao 2018;Pradhan, Mohanty, and Lal 2019). Some recent supervised methods, introduced the problem of event type detection as a sequence labeling task using a neural sequence model on a news corpus (Bekoulis et al 2019).…”

Section: Related Workmentioning

confidence: 99%

Unsupervised Detection of Sub-Events in Large Scale Disasters

Arachie

Anzaroot²,

Groves³

et al. 2020

AAAI

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Social media plays a major role during and after major natural disasters (e.g., hurricanes, large-scale fires, etc.), as people “on the ground” post useful information on what is actually happening. Given the large amounts of posts, a major challenge is identifying the information that is useful and actionable. Emergency responders are largely interested in finding out what events are taking place so they can properly plan and deploy resources. In this paper we address the problem of automatically identifying important sub-events (within a large-scale emergency “event”, such as a hurricane). In particular, we present a novel, unsupervised learning framework to detect sub-events in Tweets for retrospective crisis analysis. We first extract noun-verb pairs and phrases from raw tweets as sub-event candidates. Then, we learn a semantic embedding of extracted noun-verb pairs and phrases, and rank them against a crisis-specific ontology. We filter out noisy and irrelevant information then cluster the noun-verb pairs and phrases so that the top-ranked ones describe the most important sub-events. Through quantitative experiments on two large crisis data sets (Hurricane Harvey and the 2015 Nepal Earthquake), we demonstrate the effectiveness of our approach over the state-of-the-art. Our qualitative evaluation shows better performance compared to our baseline.

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“…Hashtag analysis is a hot topic in data mining and machine learning communities. In the last decade, many applications have been proposed such as hashtag recommendation [15], [16], hashtag-based story detection [17], [18], and microblogging retrieval [13], [19], [20]. In this research study, we focus on the hashtag retrieval problem.…”

Section: Literature Reviewmentioning

confidence: 99%

Exploring Pattern Mining Algorithms for Hashtag Retrieval Problem

et al. 2020

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Hashtag is an iconic feature to retrieve the hot topics of discussion on Twitter or other social networks. This paper incorporates the pattern mining approaches to improve the accuracy of retrieving the relevant information and speeding up the search performance. A novel algorithm called PM-HR (Pattern Mining for Hashtag Retrieval) is designed to first transform the set of tweets into a transactional database by considering two different strategies (trivial and temporal). After that, the set of the relevant patterns is discovered, and then used as a knowledge-based system for finding the relevant tweets based on users' queries under the similarity search process. Extensive results are carried out on large and different tweet collections, and the proposed PM-HR outperforms the baseline hashtag retrieval approaches in terms of runtime, and it is very competitive in terms of accuracy. INDEX TERMS Hashtag retrieval, pattern mining, scalability.

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An Encoder-Memory-Decoder Framework for Sub-Event Detection in Social Media

Cited by 10 publications

References 7 publications

A Novel Framework for Detecting Important Subevents from Crisis Events via Dynamic Semantic Graphs

A Novel Framework for Detecting Important Subevents from Crisis Events via Dynamic Semantic Graphs

Unsupervised Detection of Sub-Events in Large Scale Disasters

Exploring Pattern Mining Algorithms for Hashtag Retrieval Problem

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