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Automatic Detection and Classification of Social Events

Apoorv Agarwal, Owen Rambow
2010 Conference on Empirical Methods in Natural Language Processing  
In this paper we introduce the new task of social event extraction from text.  ...  We use syntactic and semantic insights to devise a new structure derived from dependency trees and show that this plays a role in achieving the best performing system for both social event detection and  ...  We would also like to thank Dr. Claire Monteleoni and Daniel Bauer for useful discussions and feedback. References Apoorv Agarwal, Owen Rambow, and Rebecca J Passonneau. 2010.  ... 
dblp:conf/emnlp/AgarwalR10 fatcat:obaj2huk7fhbjiqwf5vj2hmlkq

MLBiNet: A Cross-Sentence Collective Event Detection Network [article]

Dongfang Lou, Zhilin Liao, Shumin Deng, Ningyu Zhang, Huajun Chen
2021 arXiv   pre-print
The key to dealing with the problem is to encode semantic information and model event inter-dependency at a document-level.  ...  Specifically, a bidirectional decoder is firstly devised to model event inter-dependency within a sentence when decoding the event tag vector sequence.  ...  Acknowledgments We want to express gratitude to the anonymous reviewers for their hard work and kind comments.  ... 
arXiv:2105.09458v3 fatcat:dboswg2ppngfhpns7u4rpl6cby

Temporal video segmentation by event detection: A novelty detection approach

Mahesh Venkata Krishna, P. Bodesheim, M. Körner, J. Denzler
2014 Pattern Recognition and Image Analysis  
We present a novel algorithm to achieve this semantic video segmentation. The segmentation task is accomplished through event detection in a frame-by-frame processing setup.  ...  We propose using one-class classification (OCC) techniques to detect events that indicate a new segment, since they have been proved to be successful in object classification and they allow for unsupervised  ...  ACKNOWLEDGEMENTS Mahesh Venkata Krishna is funded by a grant from the Carl Zeiss AG, through the "Pro-Excellence" scholarship of the federal state of Thuringia, Germany.  ... 
doi:10.1134/s1054661814020114 fatcat:mmm56qpddvbafdjnh2dpog7zly

Biomedical event trigger detection by dependency-based word embedding

Jian Wang, Jianhai Zhang, Yuan An, Hongfei Lin, Zhihao Yang, Yijia Zhang, Yuanyuan Sun
2016 BMC Medical Genomics  
the specific task and cannot generalize to the new domain or new examples.  ...  Biomedical event trigger identification has become a research hotspot since its important role in biomedical event extraction.  ...  The data is available for public and free to use.  ... 
doi:10.1186/s12920-016-0203-8 pmid:27510445 pmcid:PMC4980775 fatcat:pdxzxesc2van5pckaf43jcvr6u

OntoED: Low-resource Event Detection with Ontology Embedding [article]

Shumin Deng, Ningyu Zhang, Luoqiu Li, Hui Chen, Huaixiao Tou, Mosha Chen, Fei Huang, Huajun Chen
2022 arXiv   pre-print
Event Detection (ED) aims to identify event trigger words from a given text and classify it into an event type.  ...  Hence, they tend to suffer from data scarcity and fail to handle new unseen event types.  ...  Acknowledgments We want to express gratitude to the anonymous reviewers for their hard work and kind comments.  ... 
arXiv:2105.10922v4 fatcat:psuv7xnyv5ff7p27gydauqsqqu

Improving Event Detection with Dependency Regularization

Kai Cao, Xiang Li, Ralph Grishman
2015 Recent Advances in Natural Language Processing  
Event Detection (ED) is an Information Extraction task which involves identifying instances of specified types of events in text.  ...  In this paper, we demonstrate the effectiveness of Dependency Regularization techniques to generalize the patterns extracted from the training data to boost ED performance.  ...  In parallel work we have shown that carefully targeted active learning of new triggers and senses can produce significant improvement in event detection at modest cost.  ... 
dblp:conf/ranlp/CaoLG15 fatcat:odsdevmfg5ai5dc2jcxf46qzei

Event Detection and Summarization on Microblogs

Jie Zhao, Shuhan Liu, Tong Cui
2016 International Journal of Grid and Distributed Computing  
Aiming to solve these problems, we propose a new approach to automatically generate event abstracts for microblog events.  ...  For extracting user opinions on an event, we propose a supervised learning model that is based on the set of long sentences extracted from microblogs.  ...  Their objective was to extract events from a large microblog data set, and then attach every new event-related post to an existing event, which often ignore the description of an event after it is extracted  ... 
doi:10.14257/ijgdc.2016.9.8.24 fatcat:zd2et7udtbhyxox33nngpofcmm

Runtime latency detection and analysis

Julien Desfossez, Mathieu Desnoyers, Michel R. Dagenais
2016 Software, Practice & Experience  
We have to use a timer to start the workqueue process, even though the workqueue is itself an independant task, because when queueing a new work, the Linux kernel informs the scheduler that the task needs  ...  The module uses a common key to track the time difference between two punctual events (the entry and the exit) and take actions depending on the delay between the two events.  ... 
doi:10.1002/spe.2389 fatcat:phkcyxrr4vgirpvlusm6wqlu74

Detecting Stance in Tweets : A Signed Network based Approach [article]

Roshni Chakraborty, Maitry Bhavsar, Sourav Kumar Dandapat, Joydeep Chandra
2022 arXiv   pre-print
In this paper, we propose a sign network based framework that use external information sources, like news articles to create a signed network of relevant entities with respect to a news event and subsequently  ...  Existing stance detection algorithms require either event specific training data or annotated twitter handles and therefore, are difficult to adapt to new events.  ...  Identification of Targets of the Event Identifying the targets with respect to a news event is important for stance detection.  ... 
arXiv:2201.07472v1 fatcat:il5ulxasqfekrdsswnzbnjvr2m

Online Social Networks Event Detection: A Survey [chapter]

Mário Cordeiro, João Gama
2016 Lecture Notes in Computer Science  
New or unforeseen events need to be identified and tracked on a real-time basis providing accurate results as quick as possible.  ...  It makes no sense to have an algorithm that provides detected event results a few hours after being announced by traditional newswire.  ...  Acknowledgements This work was supported by national funds, through the Portuguese funding agency, Fundação para a Ciência e a Tecnologia (FCT), and by European Commission through the project MAESTRA (  ... 
doi:10.1007/978-3-319-41706-6_1 fatcat:zdoso55jzjbypkye7w3uozcjce

Spatiotemporal event detection: a review

Manzhu Yu, Myra Bambacus, Guido Cervone, Keith Clarke, Daniel Duffy, Qunying Huang, Jing Li, Wenwen Li, Zhenlong Li, Qian Liu, Bernd Resch, Jingchao Yang (+1 others)
2020 International Journal of Digital Earth  
The novelty of this review is to revisit the definition and requirements of event detection and to layout the overall workflow (from sensing and event extraction methods to the operations and decision-supporting  ...  Spatiotemporal event detection serves as a gateway to enable a better understanding by detecting events that represent the abnormal status of relevant phenomena.  ...  This community paper is one of a set of efforts to setup the foundation for future spatiotemporal studies/sciences. Yang, Yu and Bambacus initialized the concept for this paper.  ... 
doi:10.1080/17538947.2020.1738569 fatcat:urbuc2zii5bajjmmkzu6idyrg4

Entity/Event-Level Sentiment Detection and Inference

Lingjia Deng
2015 Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop  
prediction models to improve detecting and inferring sentiments expressed from any entity toward any entity or event in the text, and jointly resolving various ambiguities in the entity/event-level sentiment  ...  The entity/event-level sentiment analysis is a more fine-grained and more difficult task, compared to state-of-the-art sentiment analysis work which mostly are span based.  ...  It is a stricter evaluation. Recognizing targets in news genre is also different from recognizing targets in review data.  ... 
doi:10.3115/v1/n15-2007 dblp:conf/naacl/Deng15 fatcat:akhybfj5cvhyrphmxmu3hgexp4

A Dataset of Dynamic Reverberant Sound Scenes with Directional Interferers for Sound Event Localization and Detection [article]

Archontis Politis, Sharath Adavanne, Daniel Krause, Antoine Deleforge, Prerak Srivastava, Tuomas Virtanen
2021 arXiv   pre-print
The most important difference of the new dataset is the introduction of directional interferers, meaning sound events that are localized in space but do not belong to the target classes to be detected  ...  This report presents the dataset and baseline of Task 3 of the DCASE2021 Challenge on Sound Event Localization and Detection (SELD).  ...  INTRODUCTION Sound event localization and detection (SELD) is an audio processing task that aims to jointly detect temporally target classes of sound events and localize them in space when active.  ... 
arXiv:2106.06999v2 fatcat:q3wc2p3w6zbnpgi5hqspry4g6y

English Event Detection With Translated Language Features

Sam Wei, Igor Korostil, Joel Nothman, Ben Hachey
2017 Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)  
Event detection is a complex language processing task for which it is expensive to collect training data, making generalisation challenging.  ...  We propose novel radical features from automatic translation for event extraction.  ...  Acknowledgments We wish to thank Will Radford and the anonymous reviewers for their helpful feedback. This research is funded by the Capital Markets Co-operative Research Centre.  ... 
doi:10.18653/v1/p17-2046 dblp:conf/acl/WeiKNH17 fatcat:lbut7nu6afcm5o4deordmzcl4i

Detecting and Extracting Events from Text Documents [article]

Jugal Kalita
2016 arXiv   pre-print
We also discuss applications of event detection and extraction systems, particularly in summarization, in the medical domain and in the context of Twitter posts.  ...  In particular, we look at how textual documents can be mined to extract events and ancillary information. These days, it is mostly through the application of various machine learning techniques.  ...  Therefore, Weng et al. present another approach to detect events from a corpus of Twitter messages.  ... 
arXiv:1601.04012v1 fatcat:74wtgbf2qbd6lfj34jb6heh6ei
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