Jeg er postdoktor i Språkteknologigruppen ved Universitetet i Oslo. Mine forskningsinteresser er blant annet cross-lingual og transfer learning metoder for spåk med få ressurser, og følelsesanalyse.
Publikasjoner
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Barnes, Jeremy Claude; Velldal, Erik & Øvrelid, Lilja (2020). Improving Sentiment Analysis with Multi-task Learning of Negation. Natural Language Engineering.
ISSN 1351-3249.
. doi:
10.1017/S1351324920000510
Vis sammendrag
Sentiment analysis is directly affected by compositional phenomena in language that act on the prior polarity of the words and phrases found in the text. Negation is the most prevalent of these phenomena and in order to correctly predict sentiment, a classifier must be able to identify negation and disentangle the effect that its scope has on the final polarity of a text. This paper proposes a multi-task approach to explicitly incorporate information about negation in sentiment analysis, which we show outperforms learning negation implicitly in a data-driven manner. We describe our approach, a cascading neural architecture with selective sharing of LSTM layers, and show that explicitly training the model with negation as an auxiliary task helps improve the main task of sentiment analysis. The effect is demonstrated across several different standard English-language data sets for both tasks and we analyze several aspects of our system related to its performance, varying types and amounts of input data and different multi-task setups.
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Lison, Pierre; Barnes, Jeremy; Hubin, Aliaksandr & Touileb, Samia (2020). Named Entity Recognition without Labelled Data: A Weak Supervision Approach, In Dan Jurafsky; Joyce Chai; Natalie Schluter & Joel Tetreault (ed.),
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics.
Association for Computational Linguistics.
ISBN 978-1-952148-25-5.
139.
s 1518
- 1533
Vis sammendrag
Named Entity Recognition (NER) performance often degrades rapidly when applied to target domains that differ from the texts observed during training. When in-domain labelled data is available, transfer learning techniques can be used to adapt existing NER models to the target domain. But what should one do when there is no hand-labelled data for the target domain? This paper presents a simple but powerful approach to learn NER models in the absence of labelled data through weak supervision. The approach relies on a broad spectrum of labelling functions to automatically annotate texts from the target domain. These annotations are then merged together using a hidden Markov model which captures the varying accuracies and confusions of the labelling functions. A sequence labelling model can finally be trained on the basis of this unified annotation. We evaluate the approach on two English datasets (CoNLL 2003 and news articles from Reuters and Bloomberg) and demonstrate an improvement of about 7 percentage points in entity-level F1 scores compared to an out-of-domain neural NER model.
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Navas-Alejo, Irean; Badia, Toni & Barnes, Jeremy (2020). Cross-lingual Emotion Intensity Prediction, In Malvina Nissim; Viviana Patti & Barbara Plank (ed.),
Proceedings of the Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media.
Association for Computational Linguistics.
ISBN 978-4-87974-723-5.
1.
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Øvrelid, Lilja; Mæhlum, Petter; Barnes, Jeremy & Velldal, Erik (2020). A Fine-Grained Sentiment Dataset for Norwegian, In Nicoletta Calzolari; Frédéric Béchet; Philippe Blache; Khalid Choukri; Christopher Cieri; Thierry Declerck; Sara Goggi; Hitoshi Isahara; Bente Maegaard; Joseph Mariani; Hélène Mazo; Asuncion Moreno; Jan Odijk & Stelios Piperidis (ed.),
Proceedings of The 12th Language Resources and Evaluation Conference.
European Language Resources Association.
ISBN 979-10-95546-34-4.
Article.
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Barnes, Jeremy Claude; Touileb, Samia; Øvrelid, Lilja & Velldal, Erik (2019). Lexicon information in neural sentiment analysis: a multi-task learning approach, In Mareike Hartmann & Barbara Plank (ed.),
Proceedings of the 22nd Nordic Conference on Computational Linguistics (NoDaLiDa).
Linköping University Electronic Press.
ISBN 978-91-7929-995-8.
Artikkel.
s 175
- 186
Vis sammendrag
This paper explores the use of multi-task learning (MTL) for incorporating external knowledge in neural models. Specifically, we show how MTL can enable a BiLSTM sentiment classifier to incorporate information from sentiment lexicons. Our MTL set-up is shown to improve model performance (compared to a single-task set-up) on both English and Norwegian sentence-level sentiment datasets. The paper also introduces a new sentiment lexicon for Norwegian.
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R. Atrio, Alex; Badia, Toni & Barnes, Jeremy Claude (2019). On the Effect of Word Order on Cross-lingual Sentiment Analysis. Revista de Procesamiento de Lenguaje Natural (SEPLN).
ISSN 1135-5948.
63, s 23- 30 Fulltekst i vitenarkiv.
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Barnes, Jeremy Claude (2019). LTG-Oslo Hierarchical Multi-task Network: The importance of negation for document-level sentiment in Spanish. CEUR Workshop Proceedings.
ISSN 1613-0073.
2421(5), s 378- 389
Vis sammendrag
This paper details LTG-Oslo team's participation in the sentiment track of the NEGES 2019 evaluation campaign. We participated in the task with a hierarchical multi-task network, which used shared lower-layers in a deep BiLSTM to predict negation, while the higher layers were dedicated to predicting document-level sentiment. The multi-task component shows promise as a way to incorporate information on negation into deep neural sentiment classifiers, despite the fact that the absolute results on the test set were relatively low for a binary classification task.
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Barnes, Jeremy Claude & Klinger, Roman (2019). Embedding Projection for Targeted Cross-Lingual Sentiment: Model Comparisons and a Real-World Study. The journal of artificial intelligence research.
ISSN 1076-9757.
66, s 691- 742 . doi:
10.1613/jair.1.11561
Fulltekst i vitenarkiv.
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Barnes, Jeremy Claude; Øvrelid, Lilja & Velldal, Erik (2019). Sentiment analysis is not solved! Assessing and probing sentiment classification, In Tal Linzen; Grzegorz Chrupała; Yonatan Belinkov & Dieuwke Hupkes (ed.),
The BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP at ACL 2019: Proceedings of the Second Workshop.
Association for Computational Linguistics.
ISBN 978-1-950737-30-7.
article.
s 12
- 23
Fulltekst i vitenarkiv.
Vis sammendrag
Neural methods for sentiment analysis have led to quantitative improvements over previous approaches, but these advances are not always accompanied with a thorough analysis of the qualitative differences. Therefore, it is not clear what outstanding conceptual challenges for sentiment analysis remain. In this work, we attempt to discover what challenges still prove a problem for sentiment classifiers for English and to provide a challenging dataset. We collect the subset of sentences that an (oracle) ensemble of state-of-the-art sentiment classifiers misclassify and then annotate them for 18 linguistic and paralinguistic phenomena, such as negation, sarcasm, modality, etc. Finally, we provide a case study that demonstrates the usefulness of the dataset to probe the performance of a given sentiment classifier with respect to linguistic phenomena.
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Mæhlum, Petter; Barnes, Jeremy Claude; Øvrelid, Lilja & Velldal, Erik (2019). Annotating evaluative sentences for sentiment analysis: a dataset for Norwegian, In Mareike Hartmann & Barbara Plank (ed.),
Proceedings of the 22nd Nordic Conference on Computational Linguistics (NoDaLiDa).
Linköping University Electronic Press.
ISBN 978-91-7929-995-8.
Artikkel.
s 121
- 130
Vis sammendrag
This paper documents the creation of a large-scale dataset of evaluative sentences – i.e. both subjective and objective sentences that are found to be sentiment-bearing – based on mixed-domain professional reviews from various news-sources. We present both the annotation scheme and first results for classification experiments. The effort represents a step toward creating a Norwegian dataset for fine-grained sentiment analysis.
Publisert 29. mai 2019 14:40
- Sist endret 13. des. 2019 14:33