Lilja Øvrelid

• Barnes, Jeremy Claude; Velldal, Erik & Øvrelid, Lilja (2020). Improving Sentiment Analysis with Multi-task Learning of Negation. Natural Language Engineering.  ISSN 1351-3249. Show summary

  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.

• Jørgensen, Fredrik; Aasmoe, Tobias; Husevåg, Anne-Stine Ruud; Øvrelid, Lilja & Velldal, Erik (2020). NorNE: Annotating Named Entities 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.  Papers.  s 4547 - 4556
• Pilán, Ildikó; Brekke, Pål H. & Øvrelid, Lilja (2020). Building a Norwegian Lexical Resource for Medical Entity Recognition, In Maite Melero (ed.),  Proceedings of the LREC 2020 Workshop on Multilingual Biomedical Text Processing (MultiligualBIO 2020).  European Language Resources Association (ELRA).  ISBN 979-10-95546-65-8.  KAPITTEL.  s 9 - 14 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.

• 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.
• 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 Show summary

  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.

• 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 Full text in Research Archive. Show summary

  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.

• Hammer, Hugo Lewi; Riegler, Michael Alexander; Øvrelid, Lilja & Velldal, Erik (2019). THREAT: A Large Annotated Corpus for Detection of Violent Threats, In  Proceedings of Content Based Multimedia Information (CBMI 2019).  IEEE conference proceedings.  ISBN 978-1-7281-4673-7.  1.  s 1 - 5
• 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 Nina Tahmasebi; Lars Borin; Adam Jatowt & Yang Xu (ed.),  Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change.  Association for Computational Linguistics.  ISBN 978-1-950737-31-4.  chapter.  s 196 - 201 Show summary

  We extend the well-known word analogy task to a one-to-X formulation, including one-to-none cases, when no correct answer exists. The task is cast as a relation discovery problem and applied to historical armed conflicts datasets, attempting to predict new relations of type ‘location:armed-group’ based on data about past events. As the source of semantic information, we use diachronic word embedding models trained on English news texts. A simple technique to improve diachronic performance in such task is demonstrated, using a threshold based on a function of cosine distance to decrease the number of false positives; this approach is shown to be beneficial on two different corpora. Finally, we publish a ready-to-use test set for one-to-X analogy evaluation on historical armed conflicts data.

• 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 Show summary

  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.

• Nooralahzadeh, Farhad; Lønning, Jan Tore & Øvrelid, Lilja (2019). Reinforcement-based denoising of distantly supervised NER with partial annotation, In  Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019).  Association for Computational Linguistics.  ISBN 978-1-950737-789.  Article.  s 225 - 234 Full text in Research Archive.
• Ravishankar, Vinit; Gökirmak, Memduh; Øvrelid, Lilja & Velldal, Erik (2019). Multilingual Probing of Deep Pre-Trained Contextual Encoders, In Joakim Nivre; Leon Derczynski; Filip Ginter; Bjørn Lindi; Stephan Oepen; Anders Søgaard & Jorg Tidemann (ed.),  Proceedings of the First NLPL Workshop on Deep Learning for Natural Language Processing.  Linköping University Electronic Press.  ISBN 978-91-7929-999-6.  Article.  s 37 - 47
• Ravishankar, Vinit; Øvrelid, Lilja & Velldal, Erik (2019). Probing Multilingual Sentence Representations With X-Probe, In Isabelle Augenstein; Spandana Gella; Sebastian Ruder; Burcu Can; Johannes Welbl; Xiang Ren & Marek Rei (ed.),  The 4th Workshop on Representation Learning for NLP (RepL4NLP-2019) : Proceedings of the Workshop.  Association for Computational Linguistics.  ISBN 978-1-950737-35-2.  Chapter / article.  s 156 - 168 Show summary

  This paper extends the task of probing sentence representations for linguistic insight in a multilingual domain. In doing so, we make two contributions: first, we provide datasets for multilingual probing, derived from Wikipedia, in five languages, viz. English, French, German, Spanish and Russian. Second, we evaluate six sentence encoders for each language, each trained by mapping sentence representations to English sentence representations, using sentences in a parallel corpus. We discover that cross-lingually mapped representations are often better at retaining certain linguistic information than representations derived from English encoders trained on natural language inference (NLI) as a downstream task.

• Bouma, Gosse; Hajic, Jan; Haug, Dag Trygve Truslew; Nivre, Joakim; Solberg, Per Erik & Øvrelid, Lilja (2018). Expletives in Universal Dependency Treebanks, In  Proceedings of the Second Workshop on Universal Dependencies (UDW 2018).  Association for Computational Linguistics.  ISBN 978-1-948087-780.  Artikkel.  s 18 - 26
• Fares, Murhaf; Oepen, Stephan; Øvrelid, Lilja; Björne, Jari & Johansson, Richard (2018). The 2018 Shared Task on Extrinsic Parser Evaluation: On the Downstream Utility of English Universal Dependency Parsers, In Daniel Zeman & Jan Hajic (ed.),  Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies.  Association for Computational Linguistics.  ISBN 978-1-948087-82-7.  Article.  s 22 - 33 Show summary

  We summarize empirical results and tentative conclusions from the Second Extrinsic Parser Evaluation Initiative (EPE 2018). We review the basic task setup, downstream applications involved, and end-to-end results for seventeen participating parsers. Based on both quantitative and qualitative analysis, we correlate intrinsic evaluation results at different layers of morphsyntactic analysis with observed downstream behavior.

• Kasicheyanula, Taraka Rama; Brekke, Pål Haugar; Nytrø, Øystein & Øvrelid, Lilja (2018). Iterative development of family history annotation guidelines using a synthetic corpus of clinical text, In  Proceedings of the 9th International Workshop on Health Text Mining and Information Analysis (LOUHI 2018).  Association for Computational Linguistics.  ISBN 978-1-948087-74-2.  Artikkel.  s 111 - 121
• Kutuzov, Andrei; Øvrelid, Lilja; Szymanski, Terrence & Velldal, Erik (2018). Diachronic word embeddings and semantic shifts: a survey, In  Proceedings of the 27th International Conference on Computational Linguistics.  Association for Computational Linguistics.  ISBN 978-1-948087-50-6.  conference paper.  s 1384 - 1397 Full text in Research Archive. Show summary

  Recent years have witnessed a surge of publications aimed at tracing temporal changes in lexical semantics using distributional methods, particularly prediction-based word embedding models. However, this vein of research lacks the cohesion, common terminology and shared practices of more established areas of natural language processing. In this paper, we survey the current state of academic research related to diachronic word embeddings and semantic shifts detection. We start with discussing the notion of semantic shifts, and then continue with an overview of the existing methods for tracing such time-related shifts with word embedding models. We propose several axes along which these methods can be compared, and outline the main challenges before this emerging subfield of NLP, as well as prospects and possible applications.

• Nooralahzadeh, Farhad & Øvrelid, Lilja (2018). SIRIUS-LTG: An Entity Linking Approach to Fact Extraction and Verification, In  Proceedings of the First Workshop on Fact Extraction and VERification (FEVER).  Association for Computational Linguistics.  ISBN 978-1-948087-73-5.  Article.  s 119 - 123
• Nooralahzadeh, Farhad & Øvrelid, Lilja (2018). Syntactic Dependency Representations in Neural Relation Classification, In Georgiana Dinu; Miguel Ballesteros; Avirup Sil; Sam Bowman; Wael Hamza; Anders Søgaard; Tahira Naseem & Yoav Goldberg (ed.),  Proceedings of the Workshop on the Relevance of Linguistic Structure in Neural Architectures for NLP.  Association for Computational Linguistics.  ISBN 978-1-948087-42-1.  Article.
• Nooralahzadeh, Farhad; Øvrelid, Lilja & Lønning, Jan Tore (2018). Evaluation of Domain-specific Word Embeddings using Knowledge Resources, In Nicoletta Calzolari; Khalid Choukri; Christopher Cieri; Thierry Declerck; Sara Goggi; Koiti Hasida; Hitoshi Isahara; Bente Maegaard; Joseph Mariani; Hélène Mazo; Asuncion Moreno; Jan Odijk; Stelios Piperidis & Takenobu Tokunaga (ed.),  Proceedings of the Eleventh International Conference on Language Resources and Evaluation.  European Language Resources Association.  ISBN 979-10-95546-00-9.  Article.  s 1438 - 1445 Show summary

  In this work we evaluate domain-specific embedding models induced from textual resources in the Oil and Gas domain. We conduct intrinsic and extrinsic evaluations of both general and domain-specific embeddings and we observe that constructing domain-specific word embeddings is worthwhile even with a considerably smaller corpus size. Although the intrinsic evaluation shows low performance in synonymy detection, an in-depth error analysis reveals the ability of these models to discover additional semantic relations such as hyponymy, co-hyponymy and relatedness in the target domain. Extrinsic evaluation of the embedding models is provided by a domain-specific sentence classification task, which we solve using a convolutional neural network. We further adapt embedding enhancement methods to provide vector representations for infrequent and unseen terms. Experiments show that the adapted technique can provide improvements both in intrinsic and extrinsic evaluation.

• Nooralahzadeh, Farhad; Øvrelid, Lilja & Lønning, Jan Tore (2018). SIRIUS-LTG-UiO at SemEval-2018 Task 7: Convolutional Neural Networks with Shortest Dependency Paths for Semantic Relation Extraction and Classification in Scientific Papers, In  Proceedings of the 12th International Workshop on Semantic Evaluation.  Association for Computational Linguistics.  ISBN 978-1-948087-20-9.  Article.  s 805 - 811
• Stadsnes, Cathrine; Øvrelid, Lilja & Velldal, Erik (2018). Evaluating Semantic Vectors for Norwegian. NIKT: Norsk IKT-konferanse for forskning og utdanning.  ISSN 1892-0713. Show summary

  In this article, we present two benchmark data sets for evaluating models of semantic word similarity for Norwegian. While such resources are available for English, they did not exist for Norwegian prior to this work. Furthermore, we produce large-coverage semantic vectors trained on the Norwegian Newspaper Corpus using several popular word embedding frameworks. Finally, we demonstrate the usefulness of the created resources for evaluating performance of different word embedding models on the tasks of analogical reasoning and synonym detection. The benchmark data sets and word embeddings are all made freely available.

• Velldal, Erik; Øvrelid, Lilja; Bergem, Eivind Alexander; Stadsnes, Cathrine; Touileb, Samia & Jørgensen, Fredrik (2018). NoReC: The Norwegian Review Corpus, In Nicoletta Calzolari; Khalid Choukri; Christopher Cieri; Thierry Declerck; Sara Goggi; Koiti Hasida; Hitoshi Isahara; Bente Maegaard; Joseph Mariani; Hélène Mazo; Asuncion Moreno; Jan Odijk; Stelios Piperidis & Takenobu Tokunaga (ed.),  Proceedings of the Eleventh International Conference on Language Resources and Evaluation.  European Language Resources Association.  ISBN 979-10-95546-00-9.  Article.  s 4186 - 4191 Show summary

  This paper presents the Norwegian Review Corpus (NoReC), created for training and evaluating models for document-level sentiment analysis. The full-text reviews have been collected from major Norwegian news sources and cover a range of different domains, including literature, movies, video games, restaurants, music and theater, in addition to product reviews across a range of categories. Each review is labeled with a manually assigned score of 1–6, as provided by the rating of the original author. This first release of the corpus comprises more than 35,000 reviews. It is distributed using the CoNLL-U format, pre-processed using UDPipe, along with a rich set of metadata. The work reported in this paper forms part of the SANT initiative (Sentiment Analysis for Norwegian Text), a project seeking to provide open resources and tools for sentiment analysis and opinion mining for Norwegian.

• Øvrelid, Lilja; Kåsen, Andre; Hagen, Kristin; Solberg, Per Erik; Johannessen, Janne Bondi & Nøklestad, Anders (2018). The LIA Treebank of Spoken Norwegian Dialects, In Nicoletta Calzolari; Khalid Choukri; Christopher Cieri; Thierry Declerck; Sara Goggi; Koiti Hasida; Hitoshi Isahara; Bente Maegaard; Joseph Mariani; Hélène Mazo; Asuncion Moreno; Jan Odijk; Stelios Piperidis & Takenobu Tokunaga (ed.),  Proceedings of the Eleventh International Conference on Language Resources and Evaluation.  European Language Resources Association.  ISBN 979-10-95546-00-9.  Article 642.  s 4482 - 4488 Full text in Research Archive. Show summary

  This article presents the LIA treebank of transcribed spoken Norwegian dialects. It consists of dialect recordings made in the period between 1950--1990, which have been digitised, transcribed, and subsequently annotated with morphological and dependency-style syntactic analysis as part of the LIA (Language Infrastructure made Accessible) project at the University of Oslo. In this article, we describe the LIA material of dialect recordings and its transcription, transliteration and further morphosyntactic annotation. We focus in particular on the extension of the native NDT annotation scheme to spoken language phenomena, such as pauses and various types of disfluencies, and present the subsequent conversion of the treebank to the Universal Dependencies scheme. The treebank currently consists of 13,608 tokens, distributed over 1396 segments taken from three different dialects of spoken Norwegian. The LIA treebank annotation is an on-going effort and future releases will extend on the current data set.

• Enger, Martine; Velldal, Erik & Øvrelid, Lilja (2017). An open-source tool for negation detection: a maximum-margin approach, In Eduardo Blanco; Roser Morante & Roser Saurí (ed.),  Proceedings of the Workshop on Computational Semantics Beyond Events and Roles.  Association for Computational Linguistics.  ISBN 978-1-945626-49-4.  Artikkel W17-1810.  s 64 - 69 Show summary

  This paper presents an open-source toolkit for negation detection. It identifies negation cues and their corresponding scope in either raw or parsed text using maximum-margin classification. The system design draws on best practice from the existing literature on negation detection, aiming for a simple and portable system that still achieves competitive performance. Pre-trained models and experimental results are provided for English.

• Hohle, Petter; Øvrelid, Lilja & Velldal, Erik (2017). Optimizing a PoS Tagset for Norwegian Dependency Parsing, In Jörg Tiedemann (ed.),  Proceedings of the 21st Nordic Conference on Computational Linguistics (NoDaLiDa).  Linköping University Electronic Press.  ISBN 978-91-7685-601-7.  Article no. 017.  s 142 - 151 Full text in Research Archive. Show summary

  This paper reports on a suite of experiments that evaluates how the linguistic granularity of part-of-speech tagsets impacts the performance of tagging and syntactic dependency parsing. Our results show that parsing accuracy can be significantly improved by introducing more finegrained morphological information in the tagset, even if tagger accuracy is compromised. Our taggers and parsers are trained and tested using the annotations of the Norwegian Dependency Treebank.

• Kutuzov, Andrei; Velldal, Erik & Øvrelid, Lilja (2017). Temporal dynamics of semantic relations in word embeddings: an application to predicting armed conflict participants, In  Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing.  Association for Computational Linguistics.  ISBN 978-1-945626-83-8.  chapter.  s 1825 - 1830 Full text in Research Archive. Show summary

  This paper deals with using word embedding models to trace the temporal dynamics of semantic relations between pairs of words. The set-up is similar to the well-known analogies task, but expanded with a time dimension. To this end, we apply incremental updating of the models with new training texts, including incremental vocabulary expansion, coupled with learned transformation matrices that let us map between members of the relation. The proposed approach is evaluated on the task of predicting insurgent armed groups based on geographical locations. The gold standard data for the time span 1994--2010 is extracted from the UCDP Armed Conflicts dataset. The results show that the method is feasible and outperforms the baselines, but also that important work still remains to be done.

• Kutuzov, Andrei; Velldal, Erik & Øvrelid, Lilja (2017). Tracing armed conflicts with diachronic word embedding models, In Tommaso Caselli (ed.),  Proceedings of the Events and Stories in the News Workshop.  Association for Computational Linguistics.  ISBN 978-1-945626-63-0.  Chapter.  s 31 - 36 Full text in Research Archive. Show summary

  Recent studies have shown that word embedding models can be used to trace time-related (diachronic) semantic shifts for particular words. In this paper, we evaluate some of these approaches on the new task of predicting the dynamics of global armed conflicts on a year-to-year basis, using a dataset from the field of conflict research as the gold standard and the Gigaword news corpus as the training data. The results show that much work still remains in extracting `cultural' semantic shifts from diachronic word embedding models. At the same time, we present a new task complete with an evaluation set and introduce the `anchor words' method which outperforms previous approaches on this data.

• Lapponi, Emanuele; Oepen, Stephan & Øvrelid, Lilja (2017). EPE 2017: The Sherlock Negation Resolution Downstream Application, In Stephan Oepen (ed.),  Proceedings of the 2017 Shared Task on Extrinsic Parser Evaluation at the Fourth International Conference on Dependency Linguistics and the 15th International Conference on Parsing Technologies.  Association for Computational Linguistics.  ISBN 978-1-945626-74-6.  Article.  s 25 - 30
• Oepen, Stephan; Øvrelid, Lilja; Björne, Jari; Johansson, Richard; Lapponi, Emanuele; Ginter, Filip & Velldal, Erik (2017). The 2017 Shared Task on Extrinsic Parser Evaluation: Towards a Reusable Community Infrastructure, In Stephan Oepen (ed.),  Proceedings of the 2017 Shared Task on Extrinsic Parser Evaluation at the Fourth International Conference on Dependency Linguistics and the 15th International Conference on Parsing Technologies.  Association for Computational Linguistics.  ISBN 978-1-945626-74-6.  Article.  s 1 - 16
• Sand, Heidi Marion; Velldal, Erik & Øvrelid, Lilja (2017). Wordnet extension via word embeddings: Experiments on the Norwegian Wordnet, In Jörg Tiedemann (ed.),  Proceedings of the 21st Nordic Conference on Computational Linguistics (NoDaLiDa).  Linköping University Electronic Press.  ISBN 978-91-7685-601-7.  Article no. 042.  s 298 - 302 Full text in Research Archive. Show summary

  This paper describes the process of automatically adding synsets and hypernymy relations to an existing wordnet based on word embeddings computed for POStagged lemmas in a large news corpus, achieving exact match attachment accuracy of over 80%. The reported experiments are based on the Norwegian Wordnet, but the method is language independent and also applicable to other wordnets. Moreover, this study also represents the first documented experiments of the Norwegian Wordnet.

• Velldal, Erik; Øvrelid, Lilja & Hohle, Petter (2017). Joint UD Parsing of Norwegian Bokmål and Nynorsk, In Jörg Tiedemann (ed.),  Proceedings of the 21st Nordic Conference on Computational Linguistics (NoDaLiDa).  Linköping University Electronic Press.  ISBN 978-91-7685-601-7.  Article no. 001.  s 1 - 10 Full text in Research Archive. Show summary

  This paper investigates interactions in parser performance for the two official standards for written Norwegian: Bokmål and Nynorsk. We demonstrate that while applying models across standards yields poor performance, combining the training data for both standards yields better results than previously achieved for each of them in isolation. This has immediate practical value for processing Norwegian, as it means that a single parsing pipeline is sufficient to cover both varieties, with no loss in accuracy. Based on the Norwegian Universal Dependencies treebank we present results for multiple taggers and parsers, experimenting with different ways of varying the training data given to the learners, including the use of machine translation.

• Ivanova, Angelina; Oepen, Stephan; Dridan, Rebecca; Flickinger, Dan; Øvrelid, Lilja & Lapponi, Emanuele (2016). On different approaches to syntactic analysis into bi-lexical dependencies: An empirical comparison of direct, PCFG-based, and HPSG-based parsers. Journal of Language Modelling.  ISSN 2299-856X.  4(1), s 113- 144 . doi: 10.15398/jlm.v4i1.101
• Kutuzov, Andrei; Velldal, Erik & Øvrelid, Lilja (2016). Redefining part-of-speech classes with distributional semantic models, In  Proceedings of The 20th SIGNLL Conference on Computational Natural Language Learning (CoNLL).  Association for Computational Linguistics.  ISBN 978-1-945626-19-7.  Chapter.  s 115 - 125 Full text in Research Archive.
• Oepen, Stephan; Read, Jonathon; Scheffler, Tatjana; Sidarenka, Uladzimir; Stede, Manfred; Velldal, Erik & Øvrelid, Lilja (2016). OPT: Oslo–Potsdam–Teesside. Pipelining Rules, Rankers, and Classifier Ensembles for Shallow Discourse Parsing, In Nianwen Xue (ed.),  Proceedings of the CoNLL-16 Shared Task.  Association for Computational Linguistics.  ISBN 978-1-932432-66-4.  Chapter.  s 20 - 26
• Wester, Aksel Ladegård; Øvrelid, Lilja; Velldal, Erik & Hammer, Hugo Lewi (2016). Threat detection in online discussions, In  Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis WASSA@NAACL-HLT 2016 (WASSA 2016).  Association for Computational Linguistics.  ISBN 978-1-941643-82-2.  Artikkel.  s 66 - 71
• Øvrelid, Lilja & Hohle, Petter (2016). Universal Dependencies for Norwegian, In  Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016).  European Language Resources Association.  ISBN 978-2-9517408-9-1.  Artikkel.  s 1579 - 1585
• Lien, Jostein; Velldal, Erik & Øvrelid, Lilja (2015). Improving cross-domain dependency parsing with dependency-derived clusters, In Beáta Megyesi (ed.),  Proceedings of the 20th Nordic Conference of Computational Linguistics.  Linköping University Electronic Press.  ISBN 978-91-7519-098-3.  Artikkel.  s 117 - 126 Full text in Research Archive.
• Hammer, Hugo Lewi; Solberg, Per Erik & Øvrelid, Lilja (2014). Sentiment classification of online political discussions: a comparison of a word-based and dependency-based method, In Alexandra Balahur (ed.),  5th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis.  Association for Computational Linguistics.  ISBN 978-1-634392-06-8.  1.  s 90 - 96 Full text in Research Archive.
• Solberg, Per Erik; Skjærholt, Arne; Øvrelid, Lilja; Hagen, Kristin & Johannessen, Janne Bondi (2014). The Norwegian Dependency Treebank, In Nicoletta Calzolari; Khalid Choukri; Thierry Declerck; Hrafn Loftsson; Bente Maegaard; Joseph Mariani; Asuncion Moreno; Jan Odijk & Stelios Piperidis (ed.),  Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14).  European Language Resources Association.  ISBN 978-2-9517408-8-4.  Kapittel.  s 789 - 795 Full text in Research Archive.
• Ivanova, Angelina; Oepen, Stephan; Dridan, Rebecca; Flickinger, Dan & Øvrelid, Lilja (2013). On Different Approaches to Syntactic Analysis Into Bi-Lexical Dependencies. An Empirical Comparison of Direct, PCFG-Based, and HPSG-Based Parsers, In Harry Bunt; Khalil Sima’an & Liang Huang (ed.),  Proceedings of The 13th International Conference on Parsing Technologies IWPT-2013.  Association for Computational Linguistics.  ISBN 978-1-937284-76-3.  8. Full text in Research Archive.
• Ivanova, Angelina; Oepen, Stephan & Øvrelid, Lilja (2013). Survey on parsing three dependency representations for English, In Anik Dey; Sebastian Krause; Ivelina Nikolova; Eva Vecchi; Steven Bethard; Preslav I. Nakov & Feiyu Xu (ed.),  51st Annual Meeting of the Association for Computational Linguistics Proceedings of the Student Research Workshop.  Association for Computational Linguistics.  ISBN 978-1-937284-53-4.  5.  s 31 - 37 Show summary

  In this paper we focus on practical is- sues of data representation for dependency parsing. We carry out an experimental comparison of (a) three syntactic depen- dency schemes; (b) three data-driven de- pendency parsers; and (c) the influence of two different approaches to lexical cate- gory disambiguation (aka tagging) prior to parsing. Comparing parsing accuracies in various setups, we study the interactions of these three aspects and analyze which configurations are easier to learn for a de- pendency parser.

• Ivanova, Angelina; Oepen, Stephan; Øvrelid, Lilja & Flickinger, Dan (2012). Who Did What to Whom? A Contrastive Study of Syntacto-Semantic Dependencies, In Nancy Ide & Fei Xia (ed.),  Proceedings of the Sixth Linguistic Annotation Workshop.  Association for Computational Linguistics.  ISBN 978-1-937284-32-9.  Chapter.  s 2 - 11
• Lapponi, Emanuele; Read, Jonathon & Øvrelid, Lilja (2012). Representing and Resolving Negation for Sentiment Analysis, In  The 12th IEEE International Conference on Data Mining Workshops.  IEEE.  ISBN 978-0-7695-4925-5.  artikkel.  s 687 - 692
• Lapponi, Emanuele; Velldal, Erik; Øvrelid, Lilja & Read, Jonathon (2012). UiO2: Sequence-labeling Negation Using Dependency Features, In Eneko Agirre; Johan Bos & Mona Diab (ed.),  *SEM 2012: The First Joint Conference on Lexical and Computational Semantics --- Volume 1: Proceedings of the main conference and the shared task.  Association for Computational Linguistics.  ISBN 978-1-937284-21-3.  paper.  s 319 - 327
• Read, Jonathon; Flickinger, Dan; Dridan, Rebecca; Oepen, Stephan & Øvrelid, Lilja (2012). The WeSearch Corpus, Treebank and Treecache: A Comprehensive Sample of User-Generated Content, In Nicoletta Calzolari; Khalid Choukri; Thierry Declerck; Mehmet Ugur Dogan; Bente Maegaard; Joseph Mariani & Jan Odijk (ed.),  Proceedings of the Eighth International Conference on Language Resources and Evaluation.  European Language Resources Association.  ISBN 978-2-9517408-7-7.  paper.
• Read, Jonathon; Velldal, Erik & Øvrelid, Lilja (2012). Labeling Emotions in Suicide Notes: Cost-Sensitive Learning with Heterogeneous Features. Biomedical Informatics Insights.  ISSN 1178-2226.  5, s 99- 103 . doi: 10.4137/BII.S8930
• Read, Jonathon; Velldal, Erik & Øvrelid, Lilja (2012). Topic classification for Suicidology. Journal of Computing Science and Engineering.  ISSN 1976-4677.  6(2), s 143- 150 . doi: 10.5626/JCSE.2012.6.2.143
• Read, Jonathon; Velldal, Erik; Øvrelid, Lilja & Oepen, Stephan (2012). UiO1: Constituent-Based Discriminative Ranking for Negation Resolution, In Eneko Agirre; Johan Bos & Mona Diab (ed.),  *SEM 2012: The First Joint Conference on Lexical and Computational Semantics --- Volume 1: Proceedings of the main conference and the shared task.  Association for Computational Linguistics.  ISBN 978-1-937284-21-3.  paper.  s 310 - 318
• Skjærholt, Arne & Øvrelid, Lilja (2012). Impact of Treebank Characteristics on Cross-Lingual Parser Adaptation, In Iris Hendrickx; Sandra Kübler & Kiril Simov (ed.),  Proceedings of the Eleventh International Workshop on Treebanks and Linguistic Theories (TLT11).  Edições Colibri.  ISBN 978‐989‐689‐274‐6.  Article.  s 187 - 198
• Velldal, Erik; Øvrelid, Lilja; Read, Jonathon & Oepen, Stephan (2012). Speculation and Negation: Rules, Rankers, and the Role of Syntax. Computational Linguistics.  ISSN 0891-2017.  38(2), s 369- 410 . doi: 10.1162/COLI_a_00126 Show summary

  This article explores a combination of deep and shallow approaches to the problem of resolving the scope of speculation and negation within a sentence, specifically in the domain of biomedical research literature. The first part of the article focuses on speculation. After first showing how speculation cues can be accurately identified using a very simple classifier informed only by local lexical context, we go on to explore two different syntactic approaches to resolving the in-sentence scopes of these cues. Whereas one uses manually crafted rules operating over dependency structures, the other automatically learns a discriminative ranking function over nodes in constituent trees. We provide an in-depth error analysis and discussion of various linguistic properties characterizing the problem, and show that although both approaches perform well in isolation, even better results can be obtained by combining them, yielding the best published results to date on the CoNLL-2010 Shared Task data. The last part of the article describes how our speculation system is ported to also resolve the scope of negation.With only modest modifications to the initial design, the system obtains state-of-the-art results on this task also.

• Øvrelid, Lilja & Skjærholt, Arne (2012). Lexical Categories for Improved Parsing of Web Data. International Conference on Computational Linguistics (COLING). Proceedings.  ISSN 1525-2477.  s 903- 912
• Read, Jonathon; Velldal, Erik; Oepen, Stephan & Øvrelid, Lilja (2011). Resolving speculation and negation scope in biomedical articles with a syntactic constituent ranker, In Helena Hong Gao & Minghui Dong (ed.),  Proceedings of the 25th Pacific Asia Conference on Language, Information and Computation, and the Fourth International Symposium on Languages in Biology and Medicine.  Pacific Asia Conference on Language, Information and Computation.  ISBN 978-4-905166-02-3.  Conference Paper.
• Read, Jonathon; Velldal, Erik & Øvrelid, Lilja (2011). Topic classification for suicidology, In Helena Hong Gao & Minghui Dong (ed.),  Proceedings of the 25th Pacific Asia Conference on Language, Information and Computation, and the Fourth International Symposium on Languages in Biology and Medicine.  Pacific Asia Conference on Language, Information and Computation.  ISBN 978-4-905166-02-3.  Conference Paper.  s 78 - 85
• Bouma, Gerlof; Øvrelid, Lilja & Jonas, Kuhn (2010). Towards a Large Parallel Corpus of Cleft Constructions, In Nicoletta Calzolari; Khalid Choukri; Bente Maegaard; Joseph Mariani; Jan Odijk; Stelios Piperidis; Mike Rosner & Daniel Tapias (ed.),  Proceedings of the Seventh conference on International Language Resources and Evaluation (LREC'10).  European Language Resources Association.  ISBN 2-9517408-6-7.  Del av bok.
• Seeker, Wolfgang; Bernd, Bohnet; Øvrelid, Lilja & Jonas, Kuhn (2010). Informed ways of improving data-driven dependency parsing for German, In Dan Jurafsky (ed.),  Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010).  Daheng Electronic Press.  ISBN 978-7-900268-00-6.  Del av bok.  s 1122 - 1130
• Spreyer, Kathrin; Øvrelid, Lilja & Kuhn, Jonas (2010). Training Parsers on Partial Trees: A Cross-language Comparison, In Nicoletta Calzolari; Khalid Choukri; Bente Maegaard; Joseph Mariani; Jan Odijk; Stelios Piperidis; Mike Rosner & Daniel Tapias (ed.),  Proceedings of the Seventh conference on International Language Resources and Evaluation (LREC'10).  European Language Resources Association.  ISBN 2-9517408-6-7.  Del av bok.
• Velldal, Erik; Øvrelid, Lilja & Oepen, Stephan (2010). Resolving Speculation: MaxEnt Cue Classification and Dependency-Based Scope Rules, In Richárd Farkas (ed.),  Proceedings of the Fourteenth Conference on Computational Natural Language Learning -- Shared Task.  Association for Computational Linguistics.  ISBN 978-1-932432-84-8.  artikkel nr. 7.  s 48 - 55
• Øvrelid, Lilja; Velldal, Erik & Oepen, Stephan (2010). Syntactic Scope Resolution in Uncertainty Analysis, In Dan Jurafsky (ed.),  Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010).  Daheng Electronic Press.  ISBN 978-7-900268-00-6.  artikkel.  s 1379 - 1387
• Øvrelid, Lilja; Spreyer, Kathrin & Kuhn, Jonas (2009). Cross-framework parser stacking for data-driven dependency parsing. Revue TAL.  ISSN 1248-9433.  50(3), s 109- 138
• Øvrelid, Lilja; Spreyer, Kathrin & Kuhn, Jonas (2009). Improving Data-Driven Dependency Parsing Using Large-Scale LFG Grammars, In Jian Su & Janyce Wiebe (ed.),  Proceedings of the 47th Annual Meeting of ACL and 4th International Joint Conference on NLP (ACL-IJCNLP 2009).  Association for Computational Linguistics.  ISBN 978-1-932432-45-9.  Del av bok.

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• 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.

• Dahl, Fredrik Andreas; Kasicheyanula, Taraka Rama; Hurlen, Petter; Brekke, Pål Haugar; Husby, Haldor; Gundersen, Tore; Nytrø, Øystein & Øvrelid, Lilja (2019). Classifying Norwegian radiology reports with deep learning.
• Øvrelid, Lilja (2019). Forskjeller mellom talemål og skrift. Hva kan trebanker fortelle oss?.
• Øvrelid, Lilja (2012). Constituent-based discriminative ranking for negation resolution.
• Øvrelid, Lilja (2010). Syntactic Scope Resolution in Uncertainty Analysis.
• Øvrelid, Lilja (2010). Towards a Large Parallel Corpus of Cleft Constructions.
• Øvrelid, Lilja (2010). Training Parsers on Partial Trees: A Cross-language Comparison.

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