Arkaitz Zubiaga


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NUAA-QMUL at SemEval-2020 Task 8: Utilizing BERT and DenseNet for Internet Meme Emotion Analysis
Xiaoyu Guo | Jing Ma | Arkaitz Zubiaga
Proceedings of the Fourteenth Workshop on Semantic Evaluation

This paper describes our contribution to SemEval 2020 Task 8: Memotion Analysis. Our system learns multi-modal embeddings from text and images in order to classify Internet memes by sentiment. Our model learns text embeddings using BERT and extracts features from images with DenseNet, subsequently combining both features through concatenation. We also compare our results with those produced by DenseNet, ResNet, BERT, and BERT-ResNet. Our results show that image classification models have the potential to help classifying memes, with DenseNet outperforming ResNet. Adding text features is however not always helpful for Memotion Analysis.

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Detection and Resolution of Rumors and Misinformation with NLP
Leon Derczynski | Arkaitz Zubiaga
Proceedings of the 28th International Conference on Computational Linguistics: Tutorial Abstracts

Detecting and grounding false and misleading claims on the web has grown to form a substantial sub-field of NLP. The sub-field addresses problems at multiple different levels of misinformation detection: identifying check-worthy claims; tracking claims and rumors; rumor collection and annotation; grounding claims against knowledge bases; using stance to verify claims; and applying style analysis to detect deception. This half-day tutorial presents the theory behind each of these steps as well as the state-of-the-art solutions.

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Proceedings of the 3rd International Workshop on Rumours and Deception in Social Media (RDSM)
Ahmet Aker | Arkaitz Zubiaga
Proceedings of the 3rd International Workshop on Rumours and Deception in Social Media (RDSM)


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SemEval-2019 Task 7: RumourEval, Determining Rumour Veracity and Support for Rumours
Genevieve Gorrell | Elena Kochkina | Maria Liakata | Ahmet Aker | Arkaitz Zubiaga | Kalina Bontcheva | Leon Derczynski
Proceedings of the 13th International Workshop on Semantic Evaluation

Since the first RumourEval shared task in 2017, interest in automated claim validation has greatly increased, as the danger of “fake news” has become a mainstream concern. However automated support for rumour verification remains in its infancy. It is therefore important that a shared task in this area continues to provide a focus for effort, which is likely to increase. Rumour verification is characterised by the need to consider evolving conversations and news updates to reach a verdict on a rumour’s veracity. As in RumourEval 2017 we provided a dataset of dubious posts and ensuing conversations in social media, annotated both for stance and veracity. The social media rumours stem from a variety of breaking news stories and the dataset is expanded to include Reddit as well as new Twitter posts. There were two concrete tasks; rumour stance prediction and rumour verification, which we present in detail along with results achieved by participants. We received 22 system submissions (a 70% increase from RumourEval 2017) many of which used state-of-the-art methodology to tackle the challenges involved.


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All-in-one: Multi-task Learning for Rumour Verification
Elena Kochkina | Maria Liakata | Arkaitz Zubiaga
Proceedings of the 27th International Conference on Computational Linguistics

Automatic resolution of rumours is a challenging task that can be broken down into smaller components that make up a pipeline, including rumour detection, rumour tracking and stance classification, leading to the final outcome of determining the veracity of a rumour. In previous work, these steps in the process of rumour verification have been developed as separate components where the output of one feeds into the next. We propose a multi-task learning approach that allows joint training of the main and auxiliary tasks, improving the performance of rumour verification. We examine the connection between the dataset properties and the outcomes of the multi-task learning models used.


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TDParse: Multi-target-specific sentiment recognition on Twitter
Bo Wang | Maria Liakata | Arkaitz Zubiaga | Rob Procter
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

Existing target-specific sentiment recognition methods consider only a single target per tweet, and have been shown to miss nearly half of the actual targets mentioned. We present a corpus of UK election tweets, with an average of 3.09 entities per tweet and more than one type of sentiment in half of the tweets. This requires a method for multi-target specific sentiment recognition, which we develop by using the context around a target as well as syntactic dependencies involving the target. We present results of our method on both a benchmark corpus of single targets and the multi-target election corpus, showing state-of-the art performance in both corpora and outperforming previous approaches to multi-target sentiment task as well as deep learning models for single-target sentiment.

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TOTEMSS: Topic-based, Temporal Sentiment Summarisation for Twitter
Bo Wang | Maria Liakata | Adam Tsakalidis | Spiros Georgakopoulos Kolaitis | Symeon Papadopoulos | Lazaros Apostolidis | Arkaitz Zubiaga | Rob Procter | Yiannis Kompatsiaris
Proceedings of the IJCNLP 2017, System Demonstrations

We present a system for time sensitive, topic based summarisation of the sentiment around target entities and topics in collections of tweets. We describe the main elements of the system and illustrate its functionality with two examples of sentiment analysis of topics related to the 2017 UK general election.

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SemEval-2017 Task 8: RumourEval: Determining rumour veracity and support for rumours
Leon Derczynski | Kalina Bontcheva | Maria Liakata | Rob Procter | Geraldine Wong Sak Hoi | Arkaitz Zubiaga
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

Media is full of false claims. Even Oxford Dictionaries named “post-truth” as the word of 2016. This makes it more important than ever to build systems that can identify the veracity of a story, and the nature of the discourse around it. RumourEval is a SemEval shared task that aims to identify and handle rumours and reactions to them, in text. We present an annotation scheme, a large dataset covering multiple topics – each having their own families of claims and replies – and use these to pose two concrete challenges as well as the results achieved by participants on these challenges.


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TweetMT: A Parallel Microblog Corpus
Iñaki San Vicente | Iñaki Alegría | Cristina España-Bonet | Pablo Gamallo | Hugo Gonçalo Oliveira | Eva Martínez Garcia | Antonio Toral | Arkaitz Zubiaga | Nora Aranberri
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

We introduce TweetMT, a parallel corpus of tweets in four language pairs that combine five languages (Spanish from/to Basque, Catalan, Galician and Portuguese), all of which have an official status in the Iberian Peninsula. The corpus has been created by combining automatic collection and crowdsourcing approaches, and it is publicly available. It is intended for the development and testing of microtext machine translation systems. In this paper we describe the methodology followed to build the corpus, and present the results of the shared task in which it was tested.

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Stance Classification in Rumours as a Sequential Task Exploiting the Tree Structure of Social Media Conversations
Arkaitz Zubiaga | Elena Kochkina | Maria Liakata | Rob Procter | Michal Lukasik
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Rumour stance classification, the task that determines if each tweet in a collection discussing a rumour is supporting, denying, questioning or simply commenting on the rumour, has been attracting substantial interest. Here we introduce a novel approach that makes use of the sequence of transitions observed in tree-structured conversation threads in Twitter. The conversation threads are formed by harvesting users’ replies to one another, which results in a nested tree-like structure. Previous work addressing the stance classification task has treated each tweet as a separate unit. Here we analyse tweets by virtue of their position in a sequence and test two sequential classifiers, Linear-Chain CRF and Tree CRF, each of which makes different assumptions about the conversational structure. We experiment with eight Twitter datasets, collected during breaking news, and show that exploiting the sequential structure of Twitter conversations achieves significant improvements over the non-sequential methods. Our work is the first to model Twitter conversations as a tree structure in this manner, introducing a novel way of tackling NLP tasks on Twitter conversations.

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Hawkes Processes for Continuous Time Sequence Classification: an Application to Rumour Stance Classification in Twitter
Michal Lukasik | P. K. Srijith | Duy Vu | Kalina Bontcheva | Arkaitz Zubiaga | Trevor Cohn
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)


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WarwickDCS: From Phrase-Based to Target-Specific Sentiment Recognition
Richard Townsend | Adam Tsakalidis | Yiwei Zhou | Bo Wang | Maria Liakata | Arkaitz Zubiaga | Alexandra Cristea | Rob Procter
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)


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TweetNorm_es: an annotated corpus for Spanish microtext normalization
Iñaki Alegria | Nora Aranberri | Pere Comas | Víctor Fresno | Pablo Gamallo | Lluis Padró | Iñaki San Vicente | Jordi Turmo | Arkaitz Zubiaga
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

In this paper we introduce TweetNorm_es, an annotated corpus of tweets in Spanish language, which we make publicly available under the terms of the CC-BY license. This corpus is intended for development and testing of microtext normalization systems. It was created for Tweet-Norm, a tweet normalization workshop and shared task, and is the result of a joint annotation effort from different research groups. In this paper we describe the methodology defined to build the corpus as well as the guidelines followed in the annotation process. We also present a brief overview of the Tweet-Norm shared task, as the first evaluation environment where the corpus was used.


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Analysis and Enhancement of Wikification for Microblogs with Context Expansion
Taylor Cassidy | Heng Ji | Lev-Arie Ratinov | Arkaitz Zubiaga | Hongzhao Huang
Proceedings of COLING 2012

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Tweet Ranking Based on Heterogeneous Networks
Hongzhao Huang | Arkaitz Zubiaga | Heng Ji | Hongbo Deng | Dong Wang | Hieu Le | Tarek Abdelzaher | Jiawei Han | Alice Leung | John Hancock | Clare Voss
Proceedings of COLING 2012


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Is Unlabeled Data Suitable for Multiclass SVM-based Web Page Classification?
Arkaitz Zubiaga | Víctor Fresno | Raquel Martínez
Proceedings of the NAACL HLT 2009 Workshop on Semi-supervised Learning for Natural Language Processing