Jeremy Barnes

• Samuel, David; Barnes, Jeremy; Kurtz, Robin; Oepen, Stephan; Øvrelid, Lilja & Velldal, Erik (2022). Direct parsing to sentiment graphs. Association for Computational Linguistics (ACL). Annual Meeting Conference Proceedings. ISSN 0736-587X. 2(60), s. 470–478. doi: 10.18653/v1/2022.acl-short.51.
• Moore, Andrew & Barnes, Jeremy Claude (2021). Multi-task Learning of Negation and Speculation for Targeted Sentiment Classification, Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics. ISSN 978-1-954085-46-6. s. 2838–2869.
• Lison, Pierre; Barnes, Jeremy & Hubin, Aliaksandr (2021). skweak: Weak Supervision Made Easy for NLP. I Ji, Heng & Park, Jong (Red.), Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations. Association for Computational Linguistics. ISSN 978-1-954085-56-5. s. 337–346. doi: 10.18653/v1/2021.acl-demo.40. Fulltekst i vitenarkiv
• Touileb, Samia & Barnes, Jeremy (2021). The interplay between language similarity and script on a novel multi-layer Algerian dialect corpus. I Zong, Chengqing; Xia, Fei; Li, Wenjie & Navigli, Roberto (Red.), Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021. Association for Computational Linguistics. ISSN 978-1-954085-54-1. s. 3700–3712. doi: 10.18653/v1/2021.findings-acl.324.
• Barnes, Jeremy; Kurtz, Robin; Oepen, Stephan; Øvrelid, Lilja & Velldal, Erik (2021). Structured Sentiment Analysis as Dependency Graph Parsing. I Zong, Chengqing; Xia, Fei; Li, Wenjie & Navigli, Roberto (Red.), Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Association for Computational Linguistics. ISSN 978-1-954085-52-7. s. 3387–3402. doi: http%3A/dx.doi.org/10.18653/v1/2021.acl-long.263.
• Barnes, Jeremy; Mæhlum, Petter & Touileb, Samia (2021). NorDial: A Preliminary Corpus of Written Norwegian Dialect Use. I Dobnik, Simon & Øvrelid, Lilja (Red.), Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa). Linköping University Electronic Press. ISSN 978-91-7929-614-8. s. 445–451.
• Barnes, Jeremy; Øvrelid, Lilja & Velldal, Erik (2021). If you've got it, flaunt it: Making the most of fine-grained sentiment annotations. I Merlo, Paola; Tiedemann, Jörg & Tsarfaty, Reut (Red.), Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics. Association for Computational Linguistics. ISSN 978-1-954085-02-2. s. 49–62.
• Mæhlum, Petter; Barnes, Jeremy; Kurtz, Robin; Øvrelid, Lilja & Velldal, Erik (2021). Negation in Norwegian: an annotated dataset . I Dobnik, Simon & Øvrelid, Lilja (Red.), Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa). Linköping University Electronic Press. ISSN 978-91-7929-614-8. s. 299–308.
• Kutuzov, Andrei; Barnes, Jeremy; Velldal, Erik; Øvrelid, Lilja & Oepen, Stephan (2021). Large-Scale Contextualised Language Modelling for Norwegian. I Dobnik, Simon & Øvrelid, Lilja (Red.), Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa). Linköping University Electronic Press. ISSN 978-91-7929-614-8. s. 30–40.
• Navas-Alejo, Irean; Badia, Toni & Barnes, Jeremy (2020). Cross-lingual Emotion Intensity Prediction. I Nissim, Malvina; Patti, Viviana & Plank, Barbara (Red.), Proceedings of the Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media. Association for Computational Linguistics. ISSN 978-4-87974-723-5. s. 140–152.
• Øvrelid, Lilja; Mæhlum, Petter; Barnes, Jeremy & Velldal, Erik (2020). A Fine-Grained Sentiment Dataset for Norwegian. I Calzolari, Nicoletta; Béchet, Frédéric; Blache, Philippe; Choukri, Khalid; Cieri, Christopher; Declerck, Thierry; Goggi, Sara; Isahara, Hitoshi; Maegaard, Bente; Mariani, Joseph; Mazo, Hélène; Moreno, Asuncion; Odijk, Jan & Piperidis, Stelios (Red.), Proceedings of The 12th Language Resources and Evaluation Conference. European Language Resources Association. ISSN 979-10-95546-34-4.
• Lison, Pierre; Barnes, Jeremy; Hubin, Aliaksandr & Touileb, Samia (2020). Named Entity Recognition without Labelled Data: A Weak Supervision Approach . I Jurafsky, Dan; Chai, Joyce; Schluter, Natalie & Tetreault, Joel (Red.), Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics. ISSN 978-1-952148-25-5. s. 1518–1533. Fulltekst i vitenarkiv
• Barnes, Jeremy Claude; Velldal, Erik & Øvrelid, Lilja (2020). Improving Sentiment Analysis with Multi-task Learning of Negation. Natural Language Engineering. ISSN 1351-3249. 1(21). doi: 10.1017/S1351324920000510.
• 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
• 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.
• 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
• Mæhlum, Petter; Barnes, Jeremy Claude; Øvrelid, Lilja & Velldal, Erik (2019). Annotating evaluative sentences for sentiment analysis: a dataset for Norwegian. I Hartmann, Mareike & Plank, Barbara (Red.), Proceedings of the 22nd Nordic Conference on Computational Linguistics (NoDaLiDa). Linköping University Electronic Press. ISSN 978-91-7929-995-8. s. 121–130. Fulltekst i vitenarkiv
• Barnes, Jeremy Claude; Touileb, Samia; Øvrelid, Lilja & Velldal, Erik (2019). Lexicon information in neural sentiment analysis: a multi-task learning approach. I Hartmann, Mareike & Plank, Barbara (Red.), Proceedings of the 22nd Nordic Conference on Computational Linguistics (NoDaLiDa). Linköping University Electronic Press. ISSN 978-91-7929-995-8. s. 175–186. Fulltekst i vitenarkiv
• Barnes, Jeremy Claude; Øvrelid, Lilja & Velldal, Erik (2019). Sentiment analysis is not solved! Assessing and probing sentiment classification. I Linzen, Tal; Chrupała, Grzegorz; Belinkov, Yonatan & Hupkes, Dieuwke (Red.), The BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP at ACL 2019: Proceedings of the Second Workshop. Association for Computational Linguistics. ISSN 978-1-950737-30-7. s. 12–23. doi: 10.18653/v1/w19-4802. Fulltekst i vitenarkiv

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• Barnes, Jeremy (2021). Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics. ISBN 978-1-954085-46-6. 5953 s.

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• Lison, Pierre; Barnes, Jeremy Claude & Hubin, Aliaksandr (2021). skweak: weak supervision made easy for NLP. Vis sammendrag

  We present skweak, a versatile, Python-based software toolkit enabling NLP developers to apply weak supervision to a wide range of NLP tasks. Weak supervision is an emerging machine learning paradigm based on a simple idea: instead of labelling data points by hand, we use labelling functions derived from domain knowledge to automatically obtain annotations for a given dataset. The resulting labels are then aggregated with a generative model that estimates the accuracy (and possible confusions) of each labelling function. The skweak toolkit makes it easy to implement a large spectrum of labelling functions (such as heuristics, gazetteers, neural models or linguistic constraints) on text data, apply them on a corpus, and aggregate their results in a fully unsupervised fashion. skweak is especially designed to facilitate the use of weak supervision for NLP tasks such as text classification and sequence labelling. We illustrate the use of skweak for NER and sentiment analysis. skweak is released under an open-source license and is available at https://github.com/NorskRegnesentral/skweak

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