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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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Reddit: A gold mine for personality prediction 2

Reddit: A gold mine for personality prediction

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2nd Workshop on Computational Modeling of PFople's Opinions, PersonaLity, and Emotions in Social Media, PEOPLES 2018 at the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HTL 2018

作者： Gjurković, Matej Šnajder, Jan University of Zagreb Faculty of Electrical Engineering and Computing Text Analysis and Knowledge Engineering Lab Unska 3 Zagreb10000 Croatia

ISBN: (纸本)9781948087179

Automated personality prediction from social media is gaining increasing attention in natural language processing and social sciences communities. However, due to high labeling costs and privacy issues, the few publicly available datasets are of limited size and low topic diversity. We address this problem by introducing a large-scale dataset derived from Reddit, a source so far overlooked for personality prediction. The dataset is labeled with Myers-Briggs Type Indicators (MBTI) and comes with a rich set of features for more than 9k users. We carry out a preliminary feature analysis, revealing marked differences between the MBTI dimensions and poles. Furthermore, we use the dataset to train and evaluate benchmark personality prediction models, achieving macro F1-scores between 67% and 82% on the individual dimensions and 82% accuracy for exact or one-off accurate type prediction. These results are encouraging and comparable with the reliability of standardized tests. © 2018 Association for Computational Linguistics.

关键词： Large dataset

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Takelab at SemEval-2018 Task 7: Combining Sparse and Dense Features for Relation Classification in Scientific texts 12

TakeLab at SemEval-2018 Task 7: Combining Sparse and Dense F...

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12th International Workshop on Semantic Evaluation, SemEval 2018, co-located with the 16th Annual Conference of the North American Chapter of the

作者： Gluhak, Martin di Buono, Maria Pia Akkasi, Abbas Šnajder, Jan Text Analysis and Knowledge Engineering Lab Faculty of Electrical Engineering and Computing University of Zagreb Unska 3 Zagreb10000 Croatia

ISBN: (纸本)9781948087209

We describe two systems for semantic relation classification with which we participated in the SemEval 2018 Task 7, subtask 1 on semantic relation classification: an SVM model and a CNN model. Both models combine dense pretrained word2vec features and hancrafted sparse features. For training the models, we combine the two datasets provided for the subtasks in order to balance the under-represented classes. The SVM model performed better than CNN, achieving an F1-macro score of 69.98% on subtask 1.1 and 75.69% on subtask 1.2. The system ranked 7th among 28 submissions on subtask 1.1 and 7th among 20 submissions on subtask 1.2. © 2018 Association for Computational Linguistics

关键词： Semantics

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Not Just Depressed: Bipolar Disorder Prediction on Reddit 9

Not Just Depressed: Bipolar Disorder Prediction on Reddit

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

作者： Seuklić, Ivan Gjurković, Matej Šnajder, Jan Text Analysis and Knowledge Engineering Lab Faculty of Electrical Engineering and Computing University of Zagreb Unska 3 Zagreb10000 Croatia

ISBN: (纸本)9781948087803

Bipolar disorder, an illness characterized by manic and depressive episodes, affects more than 60 million people worldwide. We present a preliminary study on bipolar disorder prediction from user-generated text on Reddit, which relies on users' self-reported labels. Our benchmark classifiers for bipolar disorder prediction outperform the baselines and reach accuracy and F1-scores of above 86%. Feature analysis shows interesting differences in language use between users with bipolar disorders and the control group, including differences in the use of emotion-expressive words. © 2018 Association for Computational Linguistics

关键词： Forecasting

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Cross-Domain Detection of Abusive Language Online 2

Cross-Domain Detection of Abusive Language Online

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2nd Workshop on Abusive Language Online, ALW 2018, co-located with EMNLP 2018

作者： Karan, Mladen Šnajder, Jan Text Analysis and Knowledge Engineering Lab Faculty of Electrical Engineering and Computing University of Zagreb Unska 3 Zagreb10000 Croatia

ISBN: (纸本)9781948087681

We investigate to what extent the models trained to detect general abusive language generalize between different datasets labeled with different abusive language types. To this end, we compare the cross-domain performance of simple classification models on nine different datasets, finding that the models fail to generalize to out-domain datasets and that having at least some in-domain data is important. We also show that using the frustratingly simple domain adaptation (Daume III, 2007) in most cases improves the results over in-domain training, especially when used to augment a smaller dataset with a larger one. © 2018 Association for Computational Linguistics

关键词： Classification (of information)

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Takelab at SemEval-2018 Task12: Argument Reasoning Comprehension with Skip-Thought Vectors 12

TakeLab at SemEval-2018 Task12: Argument Reasoning Comprehen...

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12th International Workshop on Semantic Evaluation, SemEval 2018, co-located with the 16th Annual Conference of the North American Chapter of the

作者： Brassard, Ana Kuculo, Tin Boltužić, Filip Šnajder, Jan Text Analysis and Knowledge Engineering Lab Faculty of Electrical Engineering and Computing University of Zagreb Unska 3 Zagreb10000 Croatia

ISBN: (纸本)9781948087209

This paper describes our system for the SemEval-2018 Task 12: Argument Reasoning Comprehension Task. We utilize skip-thought vectors, sentence-level distributional vectors inspired by the popular word embeddings and the skip-gram model. We encode preprocessed sentences from the dataset into vectors, then perform a binary supervised classification of the warrant that justifies the use of the reason as support for the claim. We explore a few variations of the model, reaching 54.1% accuracy on the test set, which placed us 16th out of 22 teams participating in the task. © 2018 Association for Computational Linguistics

关键词： Vectors

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Iterative Recursive Attention Model for Interpretable Sequence Classification 1

Iterative Recursive Attention Model for Interpretable Sequen...

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1st Workshop on BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP, co-located with the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018

作者： Tutek, Martin Šnajder, Jan Text Analysis and Knowledge Engineering Lab Faculty of Electrical Engineering and Computing University of Zagreb Unska 3 Zagreb10000 Croatia

ISBN: (纸本)9781948087711

Natural language processing has greatly benefited from the introduction of the attention mechanism. However, standard attention models are of limited interpretability for tasks that involve a series of inference steps. We describe an iterative recursive attention model, which constructs incremental representations of input data through reusing results of previously computed queries. We train our model on sentiment classification datasets and demonstrate its capacity to identify and combine different aspects of the input in an easily interpretable manner, while obtaining performance close to the state of the art. © 2018 Association for Computational Linguistics

关键词： Classification (of information)

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LTE-D2D for connected cars: a survey on radio resource management schemes

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Iran Journal of Computer Science 2018年第3期1卷 187-197页

作者： Harounabadi, Mehdi Mitschele-Thiel, Andreas Akkasi, Abbas Integrated Communication Systems Group Ilmenau University of Technology Ilmenau Germany Text Analysis and Knowledge Engineering Lab University of Zagreb Zagreb Croatia

Long-Term Evolution (LTE) is a promising technology to be applied for different applications due to its high penetration, high data rate, reliability, and QoS support. One of the emerging applications for LTE is vehicular networks. Vehicular networks can raise traffic safety, improve traffic management, or provide infotainment services for road users. LTE device-to-device (LTE-D2D) communication which was proposed by 3GPP working group is also a good candidate to be applied for vehicle-to-vehicle communications (V2V). The V2V communication for traffic safety has strict QoS requirements in terms of latency and reliability. LTE-D2D may provide high reliability and low latency for such a communication. However, an efficient radio resource management for V2V communications in an LTE network is required to support such requirements. In the current article, we introduce vehicular networks, their requirements, and existing technologies to be employed in them and specially look at LTE-D2D to connect cars. Most recent works in radio resource management for V2V communications in LTE are reviewed in this work. © Springer International Publishing AG, part of Springer Nature 2018.

关键词： 3gpp LTE Radio resource management V2V Vehicular networks

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Iterative recursive attention model for interpretable sequence classification

arXiv

引用

arXiv 2018年

作者： Tutek, Martin Snajder, Jan Text Analysis and Knowledge Engineering Lab Faculty of Electrical Engineering and Computing University of Zagreb Unska 3 Zagreb10000 Croatia

关键词： Natural language processing systems

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Not just depressed: Bipolar disorder prediction on reddit

arXiv

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arXiv 2018年

作者： Sekulić, Ivan Gjurković, Matej Šnajder, Jan Text Analysis and Knowledge Engineering Lab Faculty of Electrical Engineering and Computing University of Zagreb Unska 3 Zagreb10000 Croatia

关键词： Forecasting

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