Our latest publications - LTG

• Laake, Signe & Øvrelid, Lilja (2021). Forskjeller mellom talemål og skriftspråk: Hva kan trebanker fortelle oss? In Hagen, Kristin; Kristoffersen, Gjert; Vangsnes, Øystein Alexander & Åfarli, Tor Anders (Ed.), Språk i arkiva. Ny forsking om eldre talemål frå LIA-prosjektet . Novus Forlag. ISSN 9788283900811. p. 65–86.
• Yeghaneh Abkenar, Mohammad; Stede, Manfred & Oepen, Stephan (2021). Neural Argumentation Mining on Essays and Microtexts with Contextualized Word Embeddings. CEUR Workshop Proceedings. ISSN 1613-0073. 2957.
• Welzl, Michael; Oepen, Stephan; Cezary Jaskula, Cezary; Griwodz, Carsten & Islam, Safiqul (2021). Collaboration in the IETF: an initial analysis of two decades in email discussions. Computer communication review. ISSN 0146-4833. 51(3), p. 29–32. doi: 10.1145/3477482.3477488.
• Samuel, David & Straka, Milan (2021). ÚFAL at MultiLexNorm 2021: Improving Multilingual Lexical Normalization by Fine-tuning ByT5, Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021). Association for Computational Linguistics. ISSN 978-1-954085-90-9. p. 483–492.
• Kutuzov, Andrei; Giulianelli, Mario & Pivovarova, Lidia (2021). Grammatical Profiling for Semantic Change Detection, Proceedings of the 25th Conference on Computational Natural Language Learning. Association for Computational Linguistics. ISSN 978-1-955917-05-6. p. 423–434.
• 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.
• Olsen, Joakim; Næss, Arild Brandrud & Lison, Pierre (2021). Assessing the Quality of Human-Generated Summaries with Weakly Supervised Learning. In Dobnik, Simon & Øvrelid, Lilja (Ed.), Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa). Linköping University Electronic Press. ISSN 978-91-7929-614-8.
• Lison, Pierre; Barnes, Jeremy & Hubin, Aliaksandr (2021). skweak: Weak Supervision Made Easy for NLP. In Ji, Heng & Park, Jong (Ed.), 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. p. 337–346. Full text in Research Archive
• Touileb, Samia; Øvrelid, Lilja & Velldal, Erik (2021). Using Gender- and Polarity-Informed Models to Investigate Bias. In Costa-jussà, Marta R.; Gonen, Hila; Hardmeier, Christian & Webster, Kellie (Ed.), Proceedings of the 3rd Workshop on Gender Bias in Natural Language Processing. Association for Computational Linguistics. ISSN 978-1-954085-61-9. p. 66–74. doi: 10.18653/v1/2021.gebnlp-1.8.
• Touileb, Samia & Barnes, Jeremy (2021). The interplay between language similarity and script on a novel multi-layer Algerian dialect corpus. In Zong, Chengqing; Xia, Fei; Li, Wenjie & Navigli, Roberto (Ed.), Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021. Association for Computational Linguistics. ISSN 978-1-954085-54-1. p. 3700–3712. doi: 10.18653/v1/2021.findings-acl.324.
• Iazykova, Tatyana; Kapelyushnik, Denis; Bystrova, Olga & Kutuzov, Andrei (2021). Unreasonable Effectiveness of Rule-Based Heuristics in Solving Russian SuperGLUE Tasks. Komp'yuternaya Lingvistika i Intellektual'nye Tekhnologii. ISSN 2221-7932. 20, p. 302–318.
• Kutuzov, Andrei & Pivovarova, Lidia (2021). Three-part diachronic semantic change dataset for Russian, Proceedings of the 2nd International Workshop on Computational Approaches to Historical Language Change. Association for Computational Linguistics. ISSN 978-1-954085-60-2. p. 7–13.
• Lison, Pierre; Pilán, Ildikó; Sánchez, David; Batet, Montserrat & Øvrelid, Lilja (2021). Anonymisation Models for Text Data: State of the art, Challenges and Future Directions. In Zong, Chengqing; Xia, Fei; Li, Wenjie & Navigli, Roberto (Ed.), 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. p. 4188–4203. doi: 10.18653/v1/2021.acl-long.323.
• Barnes, Jeremy; Kurtz, Robin; Oepen, Stephan; Øvrelid, Lilja & Velldal, Erik (2021). Structured Sentiment Analysis as Dependency Graph Parsing. In Zong, Chengqing; Xia, Fei; Li, Wenjie & Navigli, Roberto (Ed.), 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. p. 3387–3402. doi: http%3A/dx.doi.org/10.18653/v1/2021.acl-long.263.
• Brekke, Pål Haugar; Rama, Taraka; Pilán, Ildikó; Nytrø, Øystein & Øvrelid, Lilja (2021). Synthetic data for annotation and extraction of family history information from clinical text. Journal of Biomedical Semantics. ISSN 2041-1480. 12(11). doi: 10.1186/s13326-021-00244-2.
• Garbulowski, Mateusz; Diamanti, Klev; Smolińska, Karolina; Baltzer, Nicholas; Stoll, Patricia & Bornelöv, Susanne [Show all 9 contributors for this article] (2021). R.ROSETTA: an interpretable machine learning framework. BMC Bioinformatics. ISSN 1471-2105. 22(1), p. 1–18. doi: 10.1186/s12859-021-04049-z.
• Barnes, Jeremy; Mæhlum, Petter & Touileb, Samia (2021). NorDial: A Preliminary Corpus of Written Norwegian Dialect Use. In Dobnik, Simon & Øvrelid, Lilja (Ed.), Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa). Linköping University Electronic Press. ISSN 978-91-7929-614-8. p. 445–451.
• Barnes, Jeremy; Øvrelid, Lilja & Velldal, Erik (2021). If you've got it, flaunt it: Making the most of fine-grained sentiment annotations. In Merlo, Paola; Tiedemann, Jörg & Tsarfaty, Reut (Ed.), 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. p. 49–62.
• Ravishankar, Vinit; Kutuzov, Andrei; Øvrelid, Lilja & Velldal, Erik (2021). Multilingual ELMo and the Effects of Corpus Sampling. In Dobnik, Simon & Øvrelid, Lilja (Ed.), Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa). Linköping University Electronic Press. ISSN 978-91-7929-614-8. p. 378–383.
• Mæhlum, Petter; Barnes, Jeremy; Kurtz, Robin; Øvrelid, Lilja & Velldal, Erik (2021). Negation in Norwegian: an annotated dataset . In Dobnik, Simon & Øvrelid, Lilja (Ed.), Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa). Linköping University Electronic Press. ISSN 978-91-7929-614-8. p. 299–308.
• Kutuzov, Andrei; Barnes, Jeremy; Velldal, Erik; Øvrelid, Lilja & Oepen, Stephan (2021). Large-Scale Contextualised Language Modelling for Norwegian. In Dobnik, Simon & Øvrelid, Lilja (Ed.), Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa). Linköping University Electronic Press. ISSN 978-91-7929-614-8. p. 30–40.
• Kutuzov, Andrei; Rodina, Julia; Trofimova, Yuliya & Artemova, Ekaterina (2021). ELMo and BERT in Semantic Change Detection for Russian, Proceedings of AIST 2020: Analysis of Images, Social Networks and Texts. Springer. ISSN 978-3-030-72610-2. p. 175–186.
• Kutuzov, Andrei & Kuzmenko, Elizaveta (2021). Representing ELMo embeddings as two-dimensional text online, Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations. Association for Computational Linguistics. ISSN 978-1-954085-05-3. p. 143–148.
• Dahl, Fredrik Andreas; Kasicheyanula, Taraka Rama; Hurlen, Petter; Brekke, Pål Haugar; Husby, Haldor & Gundersen, Tore [Show all 8 contributors for this article] (2021). Neural classification of Norwegian radiology reports: using NLP to detect findings in CT-scans of children. BMC Medical Informatics and Decision Making. ISSN 1472-6947. 21(1), p. 1–8. doi: 10.1186/s12911-021-01451-8.
• Samuel, David & Straka, Milan (2020). ÚFAL at MRP 2020: Permutation-invariant Semantic Parsing in PERIN. In Oepen, Stephan; Abend, Omri; Abzianidze, Lasha; Bos, Johan; Hajic, Jan; Hershcovich, Daniel; Li, Bin; O'Gorman, Tim & Zeman, Daniel (Ed.), Proceedings of the CoNLL 2020 Shared Task:Cross-Framework Meaning Representation Parsing. Association for Computational Linguistics. ISSN 9781952148644. p. 53–64. doi: 10.18653/v1/2020.conll-shared.5.
• Katricheva, Nadezda; Yaskevich, Alyaxey; Lisitsina, Anastasiya; Zhordaniya, Tamara; Kutuzov, Andrei & Kuzmenko, Elizaveta (2020). Vec2graph: A Python Library for Visualizing Word Embeddings as Graphs. Communications in Computer and Information Science. ISSN 1865-0929. 1086, p. 190–198. doi: 10.1007/978-3-030-39575-9_20.
• Kurtz, Robin; Oepen, Stephan & Kuhlmann, Marco (2020). End-to-End Negation Resolution as Graph Parsing. In Bouma, Gosse; Matsumoto, Yuji; Oepen, Stephan; Sagae, Kenji; Seddah, Djame; Sun, Weiwei; Søgaard, Anders & Tsarfaty, Reut (Ed.), Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies. Association for Computational Linguistics. ISSN 978-1-952148-11-8. p. 14–24.
• Abzianidze, Lasha; Bos, Johan & Oepen, Stephan (2020). DRS at MRP 2020: Dressing up Discourse Representation Structures as Graphs. In Oepen, Stephan; Abend, Omri; Abzianidze, Lasha; Bos, Johan; Hajic, Jan; Hershcovich, Daniel; Li, Bin; O'Gorman, Tim & Zeman, Daniel (Ed.), Proceedings of the CoNLL 2020 Shared Task:Cross-Framework Meaning Representation Parsing. Association for Computational Linguistics. ISSN 9781952148644. p. 23–32. doi: 10.18653/v1/2020.conll-shared.2.
• Oepen, Stephan; Abend, Omri; Abzianidze, Lasha; Bos, Johan; Hajic, Jan & Hershcovich, Daniel [Show all 10 contributors for this article] (2020). MRP 2020: The Second Shared Task on Cross-Framework and Cross-Lingual Meaning Representation Parsing. In Oepen, Stephan; Abend, Omri; Abzianidze, Lasha; Bos, Johan; Hajic, Jan; Hershcovich, Daniel; Li, Bin; O'Gorman, Tim & Zeman, Daniel (Ed.), Proceedings of the CoNLL 2020 Shared Task:Cross-Framework Meaning Representation Parsing. Association for Computational Linguistics. ISSN 9781952148644. p. 1–22.
• Buljan, Maja; Nivre, Joakim; Oepen, Stephan & Øvrelid, Lilja (2020). A tale of three parsers: Towards diagnostic evaluation for meaning representation parsing. In Calzolari, Nicoletta (Eds.), Proceedings of the 12th Language Resources and Evaluation Conference . European Language Resources Association. ISSN 979-10-95546-34-4. p. 1902–1909. Full text in Research Archive
• Mareček, David; Celikkanat, Hande; Silfverberg, Miika; Ravishankar, Vinit & Tiedemann, Jörg (2020). Are Multilingual Neural Machine Translation Models Better at Capturing Linguistic Features? The Prague Bulletin of Mathematical Linguistics (PBML). ISSN 0032-6585. 115, p. 143–162. doi: 10.14712/00326585.009.
• Abdou, Mostafa; Ravishankar, Vinit; Barrett, Maria; Belinkov, Yonatan; Elliott, Desmond & Søgaard, Anders (2020). The Sensitivity of Language Models and Humans to Winograd Schema Perturbations. In Jurafsky, Dan; Chai, Joyce; Schluter, Natalie & Tetreault, Joel (Ed.), Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics. ISSN 978-1-952148-25-5. p. 7590–7604. doi: 10.18653/v1/2020.acl-main.679.
• Lauscher, Anne; Ravishankar, Vinit; Vulić, Ivan & Glavaš, Goran (2020). From Zero to Hero: On the Limitations of Zero-Shot Cross-Lingual Transfer with Multilingual Transformers, Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020. Association for Computational Linguistics. ISSN 978-1-952148-60-6. p. 4483–4499. doi: 10.18653/v1/2020.emnlp-main.363.
• Kulmizev, Artur; Ravishankar, Vinit; Abdou, Mostafa & Nivre, Joakim (2020). Do Neural Language Models Show Preferences for Syntactic Formalisms? In Jurafsky, Dan; Chai, Joyce; Schluter, Natalie & Tetreault, Joel (Ed.), Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics. ISSN 978-1-952148-25-5. p. 4077–4091. doi: 10.18653/v1/2020.acl-main.375.
• Touileb, Samia (2020). LTG-ST at NADI Shared Task 1: Arabic Dialect Identification using a Stacking Classifier. In Zitouni, Imed; Abdul-Mageed, Muhammad; Bouamor, Houda; Bougares, Fethi; El-Haj, Mahmoud; Tomeh, Nadi & Zaghouani, Wajdi (Ed.), Proceedings of the Fifth Arabic Natural Language Processing Workshop. Association for Computational Linguistics. ISSN 978-1-952148-38-5. p. 313–319.
• Touileb, Samia; Øvrelid, Lilja & Velldal, Erik (2020). Gender and sentiment, critics and authors: a dataset of Norwegian book reviews. In Costa-jussà, Marta R.; Hardmeier, Christian; Radford, Will & Webster, Kellie (Ed.), Proceedings of the Second Workshop on Gender Bias in Natural Language Processing. Association for Computational Linguistics. ISSN 978-1-952148-43-9. p. 125–138.
• Kutuzov, Andrei & Giulianelli, Mario (2020). UiO-UvA at SemEval-2020 Task 1: Contextualised Embeddings for Lexical Semantic Change Detection, Proceedings of the Fourteenth Workshop on Semantic Evaluation. Association for Computational Linguistics. ISSN 978-1-952148-31-6. p. 126–134.
• Rodina, Julia & Kutuzov, Andrei (2020). RuSemShift: a dataset of historical lexical semantic change in Russian, Proceedings of the 28th International Conference on Computational Linguistics. Association for Computational Linguistics. ISSN 978-1-952148-27-9. p. 1037–1047.
• Pilán, Ildikó; Brekke, Pål Haugar; Dahl, Fredrik Andreas; Gundersen, Tore; Husby, Haldor & Nytrø, Øystein [Show all 7 contributors for this article] (2020). Classification of Syncope Cases in Norwegian Medical Records. In Rumshisky, Anna; Roberts, Kirk; Bethard, Steven & Naumann, Tristan (Ed.), Proceedings of the 3rd Clinical Natural Language Processing Workshop . Association for Computational Linguistics. ISSN 978-1-952148-74-3. p. 79–84. doi: 10.18653/v1/2020.clinicalnlp-1.9.
• Navas-Alejo, Irean; Badia, Toni & Barnes, Jeremy (2020). Cross-lingual Emotion Intensity Prediction. In Nissim, Malvina; Patti, Viviana & Plank, Barbara (Ed.), 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. p. 140–152.
• Adouane, Wafia; Touileb, Samia & Bernardy, Jean-Philippe (2020). Identifying Sentiments in Algerian Code-switched User-generated Comments. In 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 (Ed.), Proceedings of The 12th Language Resources and Evaluation Conference. European Language Resources Association. ISSN 979-10-95546-34-4. p. 2698–2705.
• Øvrelid, Lilja; Mæhlum, Petter; Barnes, Jeremy & Velldal, Erik (2020). A Fine-Grained Sentiment Dataset for Norwegian. In 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 (Ed.), 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 . In Jurafsky, Dan; Chai, Joyce; Schluter, Natalie & Tetreault, Joel (Ed.), Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics. ISSN 978-1-952148-25-5. p. 1518–1533. Full text in Research Archive
• Kunilovskaya, Maria; Kutuzov, Andrei & Plum, Alistair (2020). Taxonomy enrichment for Russian: synset classification outperforms linear hyponym-hypernym projections. Komp'yuternaya Lingvistika i Intellektual'nye Tekhnologii. ISSN 2221-7932. 19, p. 459–469.
• Kutuzov, Andrei; Fomin, Vadim; Rodina, Julia & Mikhailov, Vladislav (2020). ShiftRy: web service for diachronic analysis of Russian news. Komp'yuternaya Lingvistika i Intellektual'nye Tekhnologii. ISSN 2221-7932. 19, p. 485–501.
• Jørgensen, Fredrik; Aasmoe, Tobias; Husevåg, Anne-Stine Ruud; Øvrelid, Lilja & Velldal, Erik (2020). NorNE: Annotating Named Entities for Norwegian. In 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 (Ed.), Proceedings of The 12th Language Resources and Evaluation Conference. European Language Resources Association. ISSN 979-10-95546-34-4. p. 4547–4556.
• Pilán, Ildikó; Brekke, Pål Haugar & Øvrelid, Lilja (2020). Building a Norwegian Lexical Resource for Medical Entity Recognition. In Melero, Maite (Eds.), Proceedings of the LREC 2020 Workshop on Multilingual Biomedical Text Processing (MultiligualBIO 2020). European Language Resources Association. ISSN 979-10-95546-65-8. p. 9–14.
• Logacheva, Varvara; Teslenko, Denis; Shelmanov, Artem; Remus, S.; Ustalov, Dmitry & Kutuzov, Andrei [Show all 10 contributors for this article] (2020). Word Sense Disambiguation for 158 Languages using Word Embeddings Only. In 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 (Ed.), Proceedings of The 12th Language Resources and Evaluation Conference. European Language Resources Association. ISSN 979-10-95546-34-4. p. 5945–5954.
• 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.
• Berggren, Stig Johan; Rama, Taraka & Øvrelid, Lilja (2019). Regression or classification? Automated Essay Scoring for Norwegian. In Yannakoudakis, Helen; Kochmar, Ekaterina; Leacock, Claudia; Madnani, Nitin; Pilán, Ildikó & Zesch, Torsten (Ed.), Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications. Association for Computational Linguistics. ISSN 978-1-950737-34-5. p. 92–102.
• Koller, Alexander; Oepen, Stephan & Sun, Weiwei (2019). Graph-Based Meaning Representations: Design and Processing. In Nakov, Preslav & Palmer, Alexis (Ed.), The 57th Annual Meetingof the Association for Computational Linguistics: Tutorial Abstracts. Association for Computational Linguistics. ISSN 978-1-950737-50-5. p. 6–11. doi: 10.18653/v1/P19-4002.
• Kåsen, Andre; Hagen, Kristin; Nøklestad, Anders & Priestley, Joel (2019). Tagging a Norwegian Dialect Corpus. In Hartmann, Mareike & Plank, Barbara (Ed.), Proceedings of the 22nd Nordic Conference on Computational Linguistics (NoDaLiDa). Linköping University Electronic Press. ISSN 978-91-7929-995-8. p. 350–355. Full text in Research Archive
• Kutuzov, Andrei & Nikishina, Irina (2019). Double-Blind Peer-Reviewing and Inclusiveness in Russian NLP Conferences, Analysis of Images, Social Networks and Texts (Revised Selected Papers). Springer Publishing Company. ISSN 978-3-030-37334-4. p. 3–8. doi: 10.1007/978-3-030-37334-4_1.
• Droganova, Kira; Kutuzov, Andrei; Mediankin, Nikita & Zeman, Daniel (2019). ÚFAL-Oslo at MRP 2019: Garage Sale Semantic Parsing. In Oepen, Stephan; Abend, Omri; Hajic, Jan; Hershcovich, Daniel; Kuhlmann, Marco; O’Gorman, Tim & Nianwen, Xue (Ed.), Proceedings of the Shared Task on Cross-Framework Meaning Representation Parsing at the 2019 Conference on Natural Language Learning. Association for Computational Linguistics. ISSN 978-1-950737-60-4. p. 158–165. doi: 10.18653/v1/K19-2015.
• Oepen, Stephan & Flickinger, Dan (2019). The ERG at MRP 2019: Radically Compositional Semantic Dependencies. In Oepen, Stephan; Abend, Omri; Hajic, Jan; Hershcovich, Daniel; Kuhlmann, Marco; O’Gorman, Tim & Nianwen, Xue (Ed.), Proceedings of the Shared Task on Cross-Framework Meaning Representation Parsing at the 2019 Conference on Natural Language Learning. Association for Computational Linguistics. ISSN 978-1-950737-60-4. p. 40–44. doi: 10.18653/v1/K19-2003.
• Oepen, Stephan; Abend, Omri; Hajic, Jan; Hershcovich, Daniel; Kuhlmann, Marco & O'Gorman, Tim [Show all 10 contributors for this article] (2019). MRP 2019: Cross-Framework Meaning Representation Parsing. In Oepen, Stephan; Abend, Omri; Hajic, Jan; Hershcovich, Daniel; Kuhlmann, Marco; O’Gorman, Tim & Nianwen, Xue (Ed.), Proceedings of the Shared Task on Cross-Framework Meaning Representation Parsing at the 2019 Conference on Natural Language Learning. Association for Computational Linguistics. ISSN 978-1-950737-60-4. p. 1–27. doi: 10.18653/v1/K19-2001.
• Hammer, Hugo Lewi; Riegler, Michael Alexander; Øvrelid, Lilja & Velldal, Erik (2019). THREAT: A Large Annotated Corpus for Detection of Violent Threats, Proceedings of Content Based Multimedia Information (CBMI 2019). IEEE conference proceedings. ISSN 978-1-7281-4673-7. p. 1–5. doi: 10.1109/CBMI.2019.8877435.
• Ravishankar, Vinit; Gökirmak, Memduh; Øvrelid, Lilja & Velldal, Erik (2019). Multilingual Probing of Deep Pre-Trained Contextual Encoders. In Nivre, Joakim; Derczynski, Leon; Ginter, Filip; Lindi, Bjørn; Oepen, Stephan; Søgaard, Anders & Tidemann, Jorg (Ed.), Proceedings of the First NLPL Workshop on Deep Learning for Natural Language Processing. Linköping University Electronic Press. ISSN 978-91-7929-999-6. p. 37–47.
• Nooralahzadeh, Farhad; Lønning, Jan Tore & Øvrelid, Lilja (2019). Reinforcement-based denoising of distantly supervised NER with partial annotation, Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019). Association for Computational Linguistics. ISSN 978-1-950737-789. p. 225–234. doi: DOI%3A%2010.18653/v1/D19-6125. Full text in Research Archive
• 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, p. 691–742. doi: 10.1613/jair.1.11561. Full text in Research Archive
• 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), p. 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, p. 23–30. Full text in Research Archive
• Mæhlum, Petter; Barnes, Jeremy Claude; Øvrelid, Lilja & Velldal, Erik (2019). Annotating evaluative sentences for sentiment analysis: a dataset for Norwegian. In Hartmann, Mareike & Plank, Barbara (Ed.), Proceedings of the 22nd Nordic Conference on Computational Linguistics (NoDaLiDa). Linköping University Electronic Press. ISSN 978-91-7929-995-8. p. 121–130. Full text in Research Archive
• Barnes, Jeremy Claude; Touileb, Samia; Øvrelid, Lilja & Velldal, Erik (2019). Lexicon information in neural sentiment analysis: a multi-task learning approach. In Hartmann, Mareike & Plank, Barbara (Ed.), Proceedings of the 22nd Nordic Conference on Computational Linguistics (NoDaLiDa). Linköping University Electronic Press. ISSN 978-91-7929-995-8. p. 175–186. Full text in Research Archive
• Kutuzov, Andrei & Kuzmenko, Elizaveta (2019). To Lemmatize or Not to Lemmatize: How Word Normalisation Affects ELMo Performance in Word Sense Disambiguation. In Nivre, Joakim; Derczynski, Leon; Ginter, Filip; Lindi, Bjørn; Oepen, Stephan; Søgaard, Anders & Tidemann, Jorg (Ed.), Proceedings of the First NLPL Workshop on Deep Learning for Natural Language Processing. Linköping University Electronic Press. ISSN 978-91-7929-999-6. p. 22–28.
• Ravishankar, Vinit; Øvrelid, Lilja & Velldal, Erik (2019). Probing Multilingual Sentence Representations With X-Probe. In Augenstein, Isabelle; Gella, Spandana; Ruder, Sebastian; Can, Burcu; Welbl, Johannes; Ren, Xiang & Rei, Marek (Ed.), The 4th Workshop on Representation Learning for NLP (RepL4NLP-2019) : Proceedings of the Workshop. Association for Computational Linguistics. ISSN 978-1-950737-35-2. p. 156–168. doi: 10.18653/v1/W19-4318.
• Barnes, Jeremy Claude; Øvrelid, Lilja & Velldal, Erik (2019). Sentiment analysis is not solved! Assessing and probing sentiment classification. In Linzen, Tal; Chrupała, Grzegorz; Belinkov, Yonatan & Hupkes, Dieuwke (Ed.), 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. p. 12–23. doi: 10.18653/v1/w19-4802. Full text in Research Archive
• Kutuzov, Andrei; Dorgham, Mohammad; Oliynyk, Oleksiy; Biemann, Chris & Panchenko, Alexander (2019). Making Fast Graph-based Algorithms with Graph Metric Embeddings. In Korhonen, Anna; Traum, David & Màrquez, Lluís (Ed.), Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics. ISSN 978-1-950737-48-2. p. 3349–3355. doi: 10.18653/v1/P19-1325.
• Rodina, Julia; Bakshandaeva, Daria; Fomin, Vadim; Kutuzov, Andrei; Touileb, Samia & Velldal, Erik (2019). Measuring Diachronic Evolution of Evaluative Adjectives with Word Embeddings: the Case for English, Norwegian, and Russian. In Tahmasebi, Nina; Borin, Lars; Jatowt, Adam & Xu, Yang (Ed.), Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change. Association for Computational Linguistics. ISSN 978-1-950737-31-4. p. 202–209. doi: 10.18653/v1/W19-4725. Full text in Research Archive
• Kutuzov, Andrei; Velldal, Erik & Øvrelid, Lilja (2019). One-to-X Analogical Reasoning on Word Embeddings: a Case for Diachronic Armed Conflict Prediction from News Texts. In Tahmasebi, Nina; Borin, Lars; Jatowt, Adam & Xu, Yang (Ed.), Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change. Association for Computational Linguistics. ISSN 978-1-950737-31-4. p. 196–201. doi: 10.18653/v1/W19-4724. Full text in Research Archive
• Kutuzov, Andrei; Dorgham, Mohammad; Oliynyk, Oleksiy; Biemann, Chris & Panchenko, Alexander (2019). Learning Graph Embeddings from WordNet-based Similarity Measures. In Mihalcea, Rada; Shutova, Ekaterina; Ku, Lun-Wei; Evang, Kilian & Poria, Soujanya (Ed.), Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019). Association for Computational Linguistics. ISSN 978-1-948087-93-3. p. 125–135. doi: 10.18653/v1/S19-1014.
• Fomin, Vadim; Bakshandaeva, Daria; Rodina, Julia & Kutuzov, Andrei (2019). Tracing Cultural Diachronic Semantic Shifts in Russian Using Word Embeddings: Test Sets and Baselines. Komp'yuternaya Lingvistika i Intellektual'nye Tekhnologii. ISSN 2221-7932. 2019-May(18), p. 213–227.

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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 p.
• Dobnik, Simon & Øvrelid, Lilja (2021). Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa). Linköping University Electronic Press. ISBN 978-91-7929-614-8. 467 p.
• Kutuzov, Andrei; van der Aalst, Wil M. P.; Batagelj, Vladimir & Ignatov, Dmitry (2021). Proceedings of AIST 2020: Analysis of Images, Social Networks and Texts. Springer. ISBN 978-3-030-72610-2. 480 p.
• Bouma, Gosse; Matsumoto, Yuji; Oepen, Stephan; Sagae, Kenji; Seddah, Djame & Sun, Weiwei [Show all 8 contributors for this article] (2020). Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies. Association for Computational Linguistics. ISBN 978-1-952148-11-8. 265 p.
• Oepen, Stephan; Abend, Omri; Abzianidze, Lasha; Bos, Johan; Hajic, Jan & Hershcovich, Daniel [Show all 9 contributors for this article] (2020). Proceedings of the CoNLL 2020 Shared Task:Cross-Framework Meaning Representation Parsing. Association for Computational Linguistics. ISBN 9781952148644. 99 p.
• Oepen, Stephan; Abend, Omri; Hajic, Jan; Hershcovich, Daniel; Kuhlmann, Marco & O’Gorman, Tim [Show all 7 contributors for this article] (2019). Proceedings of the Shared Task on Cross-Framework Meaning Representation Parsing at the 2019 Conference on Natural Language Learning. Association for Computational Linguistics. ISBN 978-1-950737-60-4. 187 p.
• Nivre, Joakim; Derczynski, Leon; Ginter, Filip; Lindi, Bjørn; Oepen, Stephan & Søgaard, Anders [Show all 7 contributors for this article] (2019). Proceedings of the First NLPL Workshop on Deep Learning for Natural Language Processing. Linköping University Electronic Press. ISBN 978-91-7929-999-6. 66 p.

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• Walker, Nicholas Thomas; Dahl, Torbjørn & Lison, Pierre (2021). Dialogue Management as Graph Transformations.
• Touileb, Samia; Øvrelid, Lilja & Velldal, Erik (2021). Using Gender- and Polarity-informed Models to Investigate Bias.
• Lison, Pierre; Bølstad, Jørgen & Kvellestad, Anders (2021). Forvirrende pandemistatistikk: Hva skal vi med logaritmer i grafer? Morgenbladet. ISSN 0805-3847.
• Velldal, Erik; Walsø, Claes Lyth & Øvrelid, Lilja (2021). Nå kan maskinene tolke hva du føler og mener. [Internet]. forskning,no.
• Velldal, Erik; Walsø, Claes Lyth & Øvrelid, Lilja (2021). Nå er maskinene klare til å tolke hva du føler og mener. [Internet]. titan.uio.no.
• Korsvoll, Nils Hallvard; Elken, Mari; Sandtorv, Alexander Harald & Lison, Pierre (2021). Welcome to Norway! Khrono.no. ISSN 1894-8995.
• Lison, Pierre (2021). Skweak: Weak Supervision Made Easy for NLP.
• Lison, Pierre (2021). Fremdrift i forskningsprosjekter.
• Lison, Pierre; Pilán, Ildikó; Sánchez Ruenes, David; Batet, Montserrat & Øvrelid, Lilja (2021). Anonymisation Models for Text Data: State of the art, Challenges and Future Directions.
• Lison, Pierre; Pilán, Ildikó; Øvrelid, Lilja; Sánchez Ruenes, David & Batet, Montserrat (2021). Anonymisation Models for Text Data: State of the art, Challenges and Future Directions. Show summary

  This position paper investigates the problem of automated text anonymisation, which is a prerequisite for secure sharing of documents containing sensitive information about individuals. We summarise the key concepts behind text anonymisation and provide a review of current approaches. Anonymisation methods have so far been developed in two fields with little mutual interaction, namely natural language processing and privacy-preserving data publishing. Based on a case study, we outline the benefits and limitations of these approaches and discuss a number of open challenges, such as (1) how to account for multiple types of semantic inferences, (2) how to strike a balance between disclosure risk and data utility and (3) how to evaluate the quality of the resulting anonymisation. We lay out a case for moving beyond sequence labelling models and incorporate explicit measures of disclosure risk into the text anonymisation process.

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

  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

• Lison, Pierre & Falkum, Ingrid Lossius (2020). Hva skjedde med «Don’t be evil»? Morgenbladet. ISSN 0805-3847.
• Lison, Pierre (2020). Named Entity Recognition without Labelled Data: A Weak Supervision Approach.
• Løland, Anders; Lison, Pierre & Falkum, Ingrid Lossius (2020). Episode 6: Kan språkteknologi virkelig forstå språk? Med Ingrid Lossius Falkum og Pierre Lison. [Internet]. Sannsynligvis VIKTIG (podkast).
• Løland, Anders & Lison, Pierre (2020). Episode 5: Hva er språkteknologi (eller NLP)? Med Pierre Lison. [Internet]. Sannsynligvis VIKTIG (podkast).
• Pilán, Ildikó; Brekke, Pål H. & Øvrelid, Lilja (2020). Building a Norwegian Lexical Resource for Medical Entity Recognition. Show summary

  We present a large Norwegian lexical resource of categorized medical terms. The resource, which merges information from large medical databases, contains over 56,000 entries, including automatically mapped terms from a Norwegian medical dictionary. We describe the methodology behind this automatic dictionary entry mapping based on keywords and suffixes and further present the results of a manual evaluation performed on a subset by a domain expert. The evaluation indicated that ca. 80% of the mappings were correct.

• Prévot, Laurent; Magistry, Pierre & Lison, Pierre (2019). Should we use movie subtitles to study linguistic patterns of conversational speech? A study based on French, English and Taiwan Mandarin.
• Øvrelid, Lilja (2019). Forskjeller mellom talemål og skrift. Hva kan trebanker fortelle oss?
• Dahl, Fredrik Andreas; Kasicheyanula, Taraka Rama; Hurlen, Petter; Brekke, Pål Haugar; Husby, Haldor & Gundersen, Tore [Show all 8 contributors for this article] (2019). Classifying Norwegian radiology reports with deep learning.
• Rønningstad, Egil & Øvrelid, Lilja (2020). Targeted Sentiment Analysis for Norwegian text. Universitetet i Oslo.
• Strand, Håvard; Velldal, Erik & Landsverk, Peder (2019). Partial Automation of the Data-collection Process. University of Oslo.

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