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2018 Events and Stories in the News Workshop, EventStory 2018, colocated with the 27th International Conference on Computational Linguistics, COLING 2018

作者： Piskorski, Jakub Šarić, Fredi Zavarella, Vanni Atkinson, Martin Text and Data Mining Unit Joint Research Centre of the European Ispra Italy Text Analysis and Knowledge Engineering Lab University of Zagreb Croatia

ISBN: (纸本)9781948087599

The paper reports on exploring various machine learning techniques and a range of textual and meta-data features to train classifiers for linking related event templates automatically extracted from online news. With the best model using textual features only we achieved 94.7% (92.9%) F1 score on GOLD (SILVER) dataset. These figures were further improved to 98.6% (GOLD) and 97% (SILVER) F1 score by adding meta-data features, mainly thanks to the strong discriminatory power of automatically extracted geographical information related to events. © 2018 EventStory 2018 - Events and Stories in the News, Proceedings of the Workshop. All rights reserved.

关键词： Gold

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On the Development of an Entity-Centric Timeline Extraction Tool

On the Development of an Entity-Centric Timeline Extraction ...

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International Conference on Advances in Social Network Analysis and mining, ASONAM

作者： Jakub Piskorski Vanni Zavarella Martin Atkinson Text and Data Mining Unit Joint Research Centre of the European Commission Ispra Italy

ISBN: (纸本)9781538660522

This paper reports on an ongoing development of a tool for extracting structured information on events a given target entity participated in from massive collections of textual documents and anchoring these events on a timescale. An overview of the current version of the tool and the underlying timeline extraction process is given. Some evaluation figures that reflect system output quality are provided too. The paper will be accompanied by a live demo of the timeline extraction tool.

关键词： data mining Tools Semantics Surface morphology Task analysis Poles and towers Measurement

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Optimizing Sentinel-2 image selection in a Big data context

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Big Earth data 2017年第1期1卷 145-158页

作者： P.Kempeneers P.Soille Joint Research Centre of the European Commission Text and Data Mining UnitIspraItaly

Processing large amounts of image data such as the Sentinel-2 archive is a computationally demanding ***,for most applications,many of the images in the archive are redundant and do not contribute to the quality of the final *** optimization scheme is presented here that selects a subset of the Sentinel-2 archive in order to reduce the amount of processing,while retaining the quality of the resulting *** a case study,we focused on the creation of a cloud-free composite,covering the global land mass and based on all the images acquired from January 2016 until September *** total amount of available images was 2,128,*** selection of the optimal subset was based on quicklooks,which correspond to a spatial and spectral subset of the original Sentinel-2 products and are lossy *** selected subset contained 94,093 image tiles in total,reducing the amount of images to be processed to 4.42%of the full set.

关键词： Image selection Sentinel-2 Big data optimization

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IEST: WASSA-2018 Implicit Emotions Shared Task 9

IEST: WASSA-2018 Implicit Emotions Shared Task

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9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, WASSA 2018

作者： Klinger, Roman de Clercq, Orphée Mohammad, Saif M. Balahur, Alexandra Institut für Maschinelle Sprachverarbeitung Universität Stuttgart Germany LT3 Language and Translation Technology Team Ghent University Belgium National Research Council Canada OttawaON Canada Text and Data Mining Unit European Commission Joint Research Centre Ispra Italy

ISBN: (纸本)9781948087803

Past shared tasks on emotions use data with both overt expressions of emotions (I am so happy to see you!) as well as subtle expressions where the emotions have to be inferred, for instance from event descriptions. Further, most datasets do not focus on the cause or the stimulus of the emotion. Here, for the first time, we propose a shared task where systems have to predict the emotions in a large automatically labeled dataset of tweets without access to words denoting emotions. Based on this intention, we call this the Implicit Emotion Shared Task (IEST) because the systems have to infer the emotion mostly from the context. Every tweet has an occurrence of an explicit emotion word that is masked. The tweets are collected in a manner such that they are likely to include a description of the cause of the emotion - the stimulus. Altogether, 30 teams submitted results which range from macro F1 scores of 21 % to 71 %. The baseline (Max-Ent bag of words and bigrams) obtains an F1 score of 60 % which was available to the participants during the development phase. A study with human annotators suggests that automatic methods outperform human predictions, possibly by honing into subtle textual clues not used by humans. Corpora, resources, and results are available at the shared task website at http://***. © 2018 Association for Computational Linguistics

关键词： Large dataset

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Multi-word Entity Classification in a Highly Multilingual Environment 13

Multi-word Entity Classification in a Highly Multilingual En...

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13th Workshop on Multiword Expressions, MWE 2017 - in conjunction with the 15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017

作者： Chesney, Sophie Jacquet, Guillaume Steinberger, Ralf Piskorski, Jakub Text and Data Mining Unit Joint Research Centre European Commission Ispra Italy Cognitive Science Research Group School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom

ISBN: (纸本)9781945626487

This paper describes an approach for the classification of millions of existing multi-word entities (MWEntities), such as organisation or event names, into thirteen category types, based only on the tokens they contain. In order to classify our very large in-house collection of multilingual MWEntities into an application-oriented set of entity categories, we trained and tested distantly-supervised classifiers in 43 languages based on MWEntities extracted from BabelNet. The best-performing classifier was the multi-class SVM using a ***-weighted data representation. Interestingly, one unique classifier trained on a mix of all languages consistently performed better than classifiers trained for individual languages, reaching an averaged F1-value of 88.8%. In this paper, we present the training and test data, including a human evaluation of its accuracy, describe the methods used to train the classifiers, and discuss the results. © 2017 Association for Computational Linguistics

关键词： Classification (of information)

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IEST: WASSA-2018 implicit emotions shared task

arXiv

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arXiv 2018年

关键词： Large dataset

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