Gilles Jacobs


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Extracting Fine-Grained Economic Events from Business News
Gilles Jacobs | Veronique Hoste
Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation

Based on a recently developed fine-grained event extraction dataset for the economic domain, we present in a pilot study for supervised economic event extraction. We investigate how a state-of-the-art model for event extraction performs on the trigger and argument identification and classification. While F1-scores of above 50% are obtained on the task of trigger identification, we observe a large gap in performance compared to results on the benchmark ACE05 dataset. We show that single-token triggers do not provide sufficient discriminative information for a fine-grained event detection setup in a closed domain such as economics, since many classes have a large degree of lexico-semantic and contextual overlap.


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LT3 at SemEval-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter (hatEval)
Nina Bauwelinck | Gilles Jacobs | Véronique Hoste | Els Lefever
Proceedings of the 13th International Workshop on Semantic Evaluation

This paper describes our contribution to the SemEval-2019 Task 5 on the detection of hate speech against immigrants and women in Twitter (hatEval). We considered a supervised classification-based approach to detect hate speech in English tweets, which combines a variety of standard lexical and syntactic features with specific features for capturing offensive language. Our experimental results show good classification performance on the training data, but a considerable drop in recall on the held-out test set.


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Economic Event Detection in Company-Specific News Text
Gilles Jacobs | Els Lefever | Véronique Hoste
Proceedings of the First Workshop on Economics and Natural Language Processing

This paper presents a dataset and supervised classification approach for economic event detection in English news articles. Currently, the economic domain is lacking resources and methods for data-driven supervised event detection. The detection task is conceived as a sentence-level classification task for 10 different economic event types. Two different machine learning approaches were tested: a rich feature set Support Vector Machine (SVM) set-up and a word-vector-based long short-term memory recurrent neural network (RNN-LSTM) set-up. We show satisfactory results for most event types, with the linear kernel SVM outperforming the other experimental set-ups


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Towards an integrated pipeline for aspect-based sentiment analysis in various domains
Orphée De Clercq | Els Lefever | Gilles Jacobs | Tijl Carpels | Véronique Hoste
Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

This paper presents an integrated ABSA pipeline for Dutch that has been developed and tested on qualitative user feedback coming from three domains: retail, banking and human resources. The two latter domains provide service-oriented data, which has not been investigated before in ABSA. By performing in-domain and cross-domain experiments the validity of our approach was investigated. We show promising results for the three ABSA subtasks, aspect term extraction, aspect category classification and aspect polarity classification.


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Mental Distress Detection and Triage in Forum Posts: The LT3 CLPsych 2016 Shared Task System
Bart Desmet | Gilles Jacobs | Véronique Hoste
Proceedings of the Third Workshop on Computational Linguistics and Clinical Psychology

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Improving Text-to-Pictograph Translation Through Word Sense Disambiguation
Leen Sevens | Gilles Jacobs | Vincent Vandeghinste | Ineke Schuurman | Frank Van Eynde
Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics