Jeg er gruppeleder for Språkteknologigruppen ved Institutt for Informatikk, Universitetet i Oslo.
Faglige interesser
Forskningen min fokuserer på ulike typer syntaktisk og semantisk prosessering av tekst med bruk av maskinlæring, eksempelvis dependensparsing, negasjonsanalyse og sentimentanalyse.
Undervisning
Publikasjoner
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Hussiny, Mohammad Ali & Øvrelid, Lilja
(2023).
Emotion Analysis of Tweets Banning Education in Afghanistan.
I Barnes, Jeremy; De Clercq, Orphee & Klinger, Roman (Red.),
The 13th Workshop on Computational Approaches to
Subjectivity, Sentiment, & Social Media Analysis.
Association for Computational Linguistics.
ISSN 978-1-959429-87-6.
Fulltekst i vitenarkiv
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Mæhlum, Petter; Velldal, Erik & Øvrelid, Lilja
(2023).
A Diagnostic Dataset for Sentiment and Negation Modeling for Norwegian.
I Ilinykh, Nikolai; Morger, Felix; Dannélls, Dana; Dobnik, Simon; Megyesi, Beáta & Nivre, Joakim (Red.),
Proceedings of the Second Workshop on Resources and Representations for Under-Resourced Languages and Domains (RESOURCEFUL-2023).
Association for Computational Linguistics.
ISSN 978-1-959429-73-9.
Fulltekst i vitenarkiv
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Kolesnichenko, Larisa; Velldal, Erik & Øvrelid, Lilja
(2023).
Word Substitution with Masked Language Models as Data Augmentation for Sentiment Analysis.
I Ilinykh, Nikolai; Morger, Felix; Dannélls, Dana; Dobnik, Simon; Megyesi, Beáta & Nivre, Joakim (Red.),
Proceedings of the Second Workshop on Resources and Representations for Under-Resourced Languages and Domains (RESOURCEFUL-2023).
Association for Computational Linguistics.
ISSN 978-1-959429-73-9.
Fulltekst i vitenarkiv
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Touileb, Samia; Øvrelid, Lilja & Velldal, Erik
(2023).
Measuring normative and descriptive biases in language models using census data.
I Vlachos, Andreas & Augenstein, Isabelle (Red.),
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics.
Association for Computational Linguistics.
ISSN 978-1-959429-44-9.
Fulltekst i vitenarkiv
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Samuel, David; Kutuzov, Andrei; Øvrelid, Lilja & Velldal, Erik
(2023).
Trained on 100 million words and still in shape: BERT meets British National Corpus.
I Vlachos, Andreas & Augenstein, Isabelle (Red.),
Findings of the Association for Computational Linguistics: EACL 2023.
Association for Computational Linguistics.
ISSN 978-1-959429-47-0.
s. 1954–1974.
Vis sammendrag
While modern masked language models (LMs) are trained on ever larger corpora, we here explore the effects of down-scaling training to a modestly-sized but representative, well-balanced, and publicly available English text source – the British National Corpus. We show that pre-training on this carefully curated corpus can reach better performance than the original BERT model. We argue that this type of corpora has great potential as a language modeling benchmark. To showcase this potential, we present fair, reproducible and data-efficient comparative studies of LMs, in which we evaluate several training objectives and model architectures and replicate previous empirical results in a systematic way. We propose an optimized LM architecture called LTG-BERT.
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Mæhlum, Petter; Haug, Dag Trygve Truslew; Jørgensen, Tollef Emil; Kåsen, Andre; Nøklestad, Anders & Rønningstad, Egil
[Vis alle 9 forfattere av denne artikkelen]
(2022).
NARC – Norwegian Anaphora Resolution Corpus.
International Conference on Computational Linguistics (ICCL) (COLING).
ISSN 1525-2477.
29(7),
s. 48–60.
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Published in: Proceedings of the Fifth Workshop on Computational Models of Reference, Anaphora and Coreference (CRAC): https://aclanthology.org/venues/coling/.
We present the Norwegian Anaphora Resolution Corpus (NARC), the first publicly available corpus annotated with anaphoric relations between noun phrases for Norwegian. The paper describes the annotated data for 326 documents in Norwegian Bokmål, together with inter-annotator agreement and discussions of relevant statistics. We also present preliminary modelling results which are comparable to existing corpora for other languages, and discuss relevant problems in relation to both modelling and the annotations themselves.
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Pilán, Ildikó; Lison, Pierre; Øvrelid, Lilja; Papadopoulou, Anthi; Sánchez, David & Batet, Montserrat
(2022).
The text anonymization benchmark (TAB): A dedicated corpus and evaluation framework for text anonymization.
Computational Linguistics.
ISSN 0891-2017.
48(4),
s. 1053–1101.
doi:
10.1162/coli_a_00458.
Vis sammendrag
We present a novel benchmark and associated evaluation metrics for assessing the performance of text anonymization methods. Text anonymization, defined as the task of editing a text document to prevent the disclosure of personal information, currently suffers from a shortage of privacy-oriented annotated text resources, making it difficult to properly evaluate the level of privacy protection offered by various anonymization methods. This paper presents TAB (Text Anonymization Benchmark), a new, open-source annotated corpus developed to address this shortage. The corpus comprises 1,268 English-language court cases from the European Court of Human Rights (ECHR) enriched with comprehensive annotations about the personal information appearing in each document, including their semantic category, identifier type, confidential attributes, and co-reference relations. Compared with previous work, the TAB corpus is designed to go beyond traditional de-identification (which is limited to the detection of predefined semantic categories), and explicitly marks which text spans ought to be masked in order to conceal the identity of the person to be protected.
Along with presenting the corpus and its annotation layers, we also propose a set of evaluation metrics that are specifically tailored toward measuring the performance of text anonymization, both in terms of privacy protection and utility preservation. We illustrate the use of the benchmark and the proposed metrics by assessing the empirical performance of several baseline text anonymization models. The full corpus along with its privacy-oriented annotation guidelines, evaluation scripts, and baseline models are available on: https://github.com/NorskRegnesentral/text-anonymization-benchmark.
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Papadopoulou, Anthi; Yu, Yunhao; Lison, Pierre & Øvrelid, Lilja
(2022).
Neural Text Sanitization with Explicit Measures of Privacy Risk,
The 2nd Conference of the Asia-Pacific Chapter of the
Association for Computational Linguistics and
the 12th International Joint Conference on
Natural Language Processing.
Association for Computational Linguistics.
ISSN 978-1-955917-65-0.
s. 217–229.
Vis sammendrag
We present a novel approach for text sanitization, which is the task of editing a document to mask all (direct and indirect) personal identifiers and thereby conceal the identity of the individuals(s) mentioned in the text. In contrast to previous work, the approach relies on explicit measures of privacy risk, making it possible to explicitly control the trade-off between privacy protection and data utility. The approach proceeds in three steps. A neural, privacy-enhanced entity recognizer is first employed to detect and classify potential personal identifiers. We then determine which entities, or combination of entities, are likely to pose a re-identification risk through a range of privacy risk assessment measures. We present three such measures of privacy risk, respectively based on (1) span probabilities derived from a BERT language model, (2) web search queries and (3) a classifier trained on labelled data. Finally, a linear optimization solver decides which entities to mask to minimize the semantic loss while simultaneously ensuring that the estimated privacy risk remains under a given threshold. We evaluate the approach both in the absence and presence of manually annotated data. Our results highlight the potential of the approach, as well as issues specific types of personal data can introduce to the process.
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Rønningstad, Egil; Øvrelid, Lilja & Velldal, Erik
(2022).
Entity-Level Sentiment Analysis (ELSA): An Exploratory Task Survey.
International Conference on Computational Linguistics (ICCL) (COLING).
ISSN 1525-2477.
s. 6773–6783.
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This paper explores the task of identifying the overall sentiment expressed towards volitional entities (persons and organizations) in a document - what we refer to as Entity-Level Sentiment Analysis (ELSA). While identifying sentiment conveyed towards an entity is well researched for shorter texts like tweets, we find little to no research on this specific task for longer texts with multiple mentions and opinions towards the same entity. This lack of research would be understandable if ELSA can be derived from existing tasks and models. To assess this, we annotate a set of professional reviews for their overall sentiment towards each volitional entity in the text. We sample from data already annotated for document-level, sentence-level, and target-level sentiment in a multi-domain review corpus, and our results indicate that there is no single proxy task that provides this overall sentiment we seek for the entities at a satisfactory level of performance. We present a suite of experiments aiming to assess the contribution towards ELSA provided by document-, sentence-, and target-level sentiment analysis, and provide a discussion of their shortcomings. We show that sentiment in our dataset is expressed not only with an entity mention as target, but also towards targets with a sentiment-relevant relation to a volitional entity. In our data, these relations extend beyond anaphoric coreference resolution, and our findings call for further research of the topic. Finally, we also present a survey of previous relevant work.
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Touileb, Samia; Øvrelid, Lilja & Velldal, Erik
(2022).
Occupational Biases in Norwegian and Multilingual Language Models.
I Christine, Basta; Marta R., Costa-jussà; Hila, Gonen; Christian, Hardmeier & Gabriel, Stanovsky (Red.),
Proceedings of The 4th Workshop on Gender Bias in Natural Language Processing.
Association for Computational Linguistics.
ISSN 978-1-955917-68-1.
s. 200–211.
doi:
10.18653/v1/2022.gebnlp-1.21.
Vis sammendrag
In this paper we explore how a demographic distribution of occupations, along gender dimensions, is reflected in pre-trained language models. We give a descriptive assessment of the distribution of occupations, and investigate to what extent these are reflected in four Norwegian and two multilingual models. To this end, we introduce a set of simple bias probes, and perform five different tasks combining gendered pronouns, first names, and a set of occupations from the Norwegian statistics bureau. We show that language specific models obtain more accurate results, and are much closer to the real-world distribution of clearly gendered occupations. However, we see that none of the models have correct representations of the occupations that are demo-graphically balanced between genders. We also discuss the importance of the training data on which the models were trained on, and argue that template-based bias probes can some-times be fragile, and a simple alteration in a template can change a model’s behavior.
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Kutuzov, Andrei; Velldal, Erik & Øvrelid, Lilja
(2022).
Contextualized embeddings for semantic change detection: Lessons learned .
Northern European Journal of Language Technology (NEJLT).
ISSN 2000-1533.
8(1).
doi:
10.3384/nejlt.2000-1533.2022.3478.
Vis sammendrag
We present a qualitative analysis of the (potentially erroneous) outputs of contextualized embedding-based methods for detecting diachronic semantic change. First, we introduce an ensemble method outperforming previously described contextualized approaches. This method is used as a basis for an in-depth analysis of the degrees of semantic change predicted for English words across 5 decades. Our findings show that contextualized methods can often predict high change scores for words which are not undergoing any real diachronic semantic shift in the lexicographic sense of the term (or at least the status of these shifts is questionable). Such challenging cases are discussed in detail with examples, and their linguistic categorization is proposed. Our conclusion is that pre-trained contextualized language models are prone to confound changes in lexicographic senses and changes in contextual variance, which naturally stem from their distributional nature, but is different from the types of issues observed in methods based on static embeddings. Additionally, they often merge together syntactic and semantic aspects of lexical entities. We propose a range of possible future solutions to these issues.
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Barnes, Jeremy; Oberlaender, Laura; Troiano, Enrica; Kutuzov, Andrei; Buchmann, Jan & Agerri, Rodrigo
[Vis alle 8 forfattere av denne artikkelen]
(2022).
SemEval 2022 Task 10: Structured Sentiment Analysis.
I Emerson, Guy; Schluter, Natalie; Stanovsky, Gabriel; Kumar, Ritesh; Palmer, Alexis; Schneider, Nathan; Singh, Siddarth & Ratan, Shyam (Red.),
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022).
Association for Computational Linguistics.
ISSN 978-1-955917-80-3.
s. 1280–1295.
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Papadopoulou, Anthi; Lison, Pierre; Øvrelid, Lilja & Pilán, Ildikó
(2022).
Bootstrapping Text Anonymization Models with Distant Supervision.
I Calzolari, Nicoletta; Béchet, Frédéric; Blache, Philippe; Choukri, Khalid; Cieri, Christopher; Declerck, Thierry; Goggi, Sara; Isahara, Hitoshi; Maegaard, Bente; Mariani, Joseph; Mazo, Hélène; Odijk, Jan & Piperidis, Stelios (Red.),
Proceedings of the Thirteenth Language Resources and Evaluation Conference.
European Language Resources Association.
ISSN 979-10-95546-72-6.
s. 4477–4487.
Vis sammendrag
We propose a novel method to bootstrap text anonymization models based on distant supervision. Instead of requiring
manually labeled training data, the approach relies on a knowledge graph expressing the background information assumed to be publicly available about various individuals. This knowledge graph is employed to automatically annotate text documents including personal data about a subset of those individuals. More precisely, the method determines which text spans ought to be masked in order to guarantee k-anonymity, assuming an adversary with access to both the text documents and the background information expressed in the knowledge graph. The resulting collection of labeled documents is then used as training data to fine-tune a pre-trained language model for text anonymization. We illustrate this approach using a knowledge graph extracted from Wikidata and short biographical texts from Wikipedia. Evaluation results with a RoBERTa-based model and a manually annotated collection of 553 summaries showcase the potential of the approach, but also unveil a number of issues that may arise if the knowledge graph is noisy or incomplete. The results also illustrate that, contrary to most sequence labeling problems, the text anonymization task may admit several alternative solutions.
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Trattner, Christoph; Jannach, Dietmar; Motta, Enrico; Meijer, Irene Costera; Diakopoulos, Nicholas & Elahi, Mehdi
[Vis alle 13 forfattere av denne artikkelen]
(2021).
Responsible media technology and AI: challenges and research directions.
AI and Ethics.
ISSN 2730-5953.
doi:
10.1007/s43681-021-00126-4.
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The last two decades have witnessed major disruptions to the traditional media industry as a result of technological breakthroughs. New opportunities and challenges continue to arise, most recently as a result of the rapid advance and adoption of artificial intelligence technologies. On the one hand, the broad adoption of these technologies may introduce new opportunities for diversifying media offerings, fighting disinformation, and advancing data-driven journalism. On the other hand, techniques such as algorithmic content selection and user personalization can introduce risks and societal threats. The challenge of balancing these opportunities and benefits against their potential for negative impacts underscores the need for more research in responsible media technology. In this paper, we first describe the major challenges—both for societies and the media industry—that come with modern media technology. We then outline various places in the media production and dissemination chain, where research gaps exist, where better technical approaches are needed, and where technology must be designed in a way that can effectively support responsible editorial processes and principles. We argue that a comprehensive approach to research in responsible media technology, leveraging an interdisciplinary approach and a close cooperation between the media industry and academic institutions, is urgently needed.
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Laake, Signe & Øvrelid, Lilja
(2021).
Forskjeller mellom talemål og skriftspråk: Hva kan trebanker fortelle oss?
I Hagen, Kristin; Kristoffersen, Gjert; Vangsnes, Øystein Alexander & Åfarli, Tor Anders (Red.),
Språk i arkiva. Ny forsking om eldre talemål frå LIA-prosjektet .
Novus Forlag.
ISSN 9788283900811.
s. 65–86.
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Lison, Pierre; Pilán, Ildikó; Sánchez, David; Batet, Montserrat & Øvrelid, Lilja
(2021).
Anonymisation Models for Text Data: State of the art, Challenges and Future Directions.
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. 4188–4203.
doi:
10.18653/v1/2021.acl-long.323.
Vis sammendrag
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.
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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:/dx.doi.org/10.18653/v1/2021.acl-long.263.
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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.
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We present the ongoing NorLM initiative to support the creation and use of very large contextualised language models for Norwegian (and in principle other Nordic languages), including a ready-to-use software environment, as well as an experience report for data preparation and training. This paper introduces the first large-scale monolingual language models for Norwegian, based on both the ELMo and BERT frameworks. In addition to detailing the training process, we present contrastive benchmark results on a suite of NLP tasks for Norwegian. For additional background and access to the data, models, and software, please see: http://norlm.nlpl.eu
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Buljan, Maja; Nivre, Joakim; Oepen, Stephan & Øvrelid, Lilja
(2020).
A tale of three parsers: Towards diagnostic evaluation for meaning representation parsing.
I Calzolari, Nicoletta (Red.),
Proceedings of the 12th Language Resources and Evaluation Conference
.
European Language Resources Association.
ISSN 979-10-95546-34-4.
s. 1902–1909.
Fulltekst i vitenarkiv
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(http://www.lrec-conf.org/proceedings/lrec2020/index.html)
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Touileb, Samia; Øvrelid, Lilja & Velldal, Erik
(2020).
Gender and sentiment, critics and authors: a dataset of Norwegian book reviews.
I Costa-jussà, Marta R.; Hardmeier, Christian; Radford, Will & Webster, Kellie (Red.),
Proceedings of the Second Workshop on Gender Bias in Natural Language Processing.
Association for Computational Linguistics.
ISSN 978-1-952148-43-9.
s. 125–138.
Vis sammendrag
Gender bias in models and datasets is widely studied in NLP. The focus has usually been on analysing how females and males express themselves, or how females and males are described. However, a less studied aspect is the combination of these two perspectives, how female and male describe the same or opposite gender. In this paper, we present a new gender annotated sentiment dataset of critics reviewing the works of female and male authors. We investigate if this newly annotated dataset contains differences in how the works of male and female authors are critiqued, in particular in terms of positive and negative sentiment. We also explore the differences in how this is done by male and female critics. We show that there are differences in how critics assess the works of authors of the same or opposite gender. For example, male critics rate crime novels written by females, and romantic and sentimental works written by males, more negatively.
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Pilán, Ildikó; Brekke, Pål Haugar; Dahl, Fredrik Andreas; Gundersen, Tore; Husby, Haldor & Nytrø, Øystein
[Vis alle 7 forfattere av denne artikkelen]
(2020).
Classification of Syncope Cases in Norwegian Medical Records.
I Rumshisky, Anna; Roberts, Kirk; Bethard, Steven & Naumann, Tristan (Red.),
Proceedings of the 3rd Clinical Natural Language Processing Workshop
.
Association for Computational Linguistics.
ISSN 978-1-952148-74-3.
s. 79–84.
doi:
10.18653/v1/2020.clinicalnlp-1.9.
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Loss of consciousness, so-called syncope, is a commonly occurring symptom associated with worse prognosis for a number of heart-related diseases. We present a comparison of methods for a diagnosis classification task in Norwegian clinical notes, targeting syncope, i.e. fainting cases. We find that an often neglected baseline with keyword matching constitutes a rather strong basis, but more advanced methods do offer some improvement in classification performance, especially a convolutional neural network model. The developed pipeline is planned to be used for quantifying unregistered syncope cases in Norway.
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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.
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Jørgensen, Fredrik; Aasmoe, Tobias; Husevåg, Anne-Stine Ruud; Øvrelid, Lilja & Velldal, Erik
(2020).
NorNE: Annotating Named Entities 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.
s. 4547–4556.
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Pilán, Ildikó; Brekke, Pål Haugar & Øvrelid, Lilja
(2020).
Building a Norwegian Lexical Resource for Medical Entity Recognition.
I Melero, Maite (Red.),
Proceedings of the LREC 2020 Workshop on Multilingual Biomedical Text Processing (MultiligualBIO 2020).
European Language Resources Association.
ISSN 979-10-95546-65-8.
s. 9–14.
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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.
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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.
1(21).
doi:
10.1017/S1351324920000510.
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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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Berggren, Stig Johan; Rama, Taraka & Øvrelid, Lilja
(2019).
Regression or classification? Automated Essay Scoring for Norwegian.
I Yannakoudakis, Helen; Kochmar, Ekaterina; Leacock, Claudia; Madnani, Nitin; Pilán, Ildikó & Zesch, Torsten (Red.),
Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications.
Association for Computational Linguistics.
ISSN 978-1-950737-34-5.
s. 92–102.
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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.
s. 1–5.
doi:
10.1109/CBMI.2019.8877435.
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Ravishankar, Vinit; Gökirmak, Memduh; Øvrelid, Lilja & Velldal, Erik
(2019).
Multilingual Probing of Deep Pre-Trained Contextual Encoders.
I Nivre, Joakim; Derczynski, Leon; Ginter, Filip; Lindi, Bjørn; Oepen, Stephan; Søgaard, Anders & Tidemann, Jorg (Red.),
Proceedings of the First NLPL Workshop on Deep Learning for Natural Language Processing.
Linköping University Electronic Press.
ISSN 978-91-7929-999-6.
s. 37–47.
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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.
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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.
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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.
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.
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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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Ravishankar, Vinit; Øvrelid, Lilja & Velldal, Erik
(2019).
Probing Multilingual Sentence Representations With X-Probe.
I Augenstein, Isabelle; Gella, Spandana; Ruder, Sebastian; Can, Burcu; Welbl, Johannes; Ren, Xiang & Rei, Marek (Red.),
The 4th Workshop on Representation Learning for NLP (RepL4NLP-2019) : Proceedings of the Workshop.
Association for Computational Linguistics.
ISSN 978-1-950737-35-2.
s. 156–168.
doi:
10.18653/v1/W19-4318.
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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.
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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.
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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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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.
I Tahmasebi, Nina; Borin, Lars; Jatowt, Adam & Xu, Yang (Red.),
Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change.
Association for Computational Linguistics.
ISSN 978-1-950737-31-4.
s. 196–201.
doi:
10.18653/v1/W19-4724.
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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.
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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.
I Zeman, Daniel & Hajic, Jan (Red.),
Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies.
Association for Computational Linguistics.
ISSN 978-1-948087-82-7.
s. 22–33.
doi:
10.18653/v1/K18-2002.
Vis sammendrag
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.
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Stadsnes, Cathrine; Øvrelid, Lilja & Velldal, Erik
(2018).
Evaluating Semantic Vectors for Norwegian.
NIKT: Norsk IKT-konferanse for forskning og utdanning.
ISSN 1892-0713.
Vis sammendrag
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.
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Kutuzov, Andrei; Øvrelid, Lilja; Szymanski, Terrence & Velldal, Erik
(2018).
Diachronic word embeddings and semantic shifts: a survey,
Proceedings of the 27th International Conference on Computational Linguistics.
Association for Computational Linguistics.
ISSN 978-1-948087-50-6.
s. 1384–1397.
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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.
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Nooralahzadeh, Farhad & Øvrelid, Lilja
(2018).
Syntactic Dependency Representations in Neural Relation Classification.
I Dinu, Georgiana; Ballesteros, Miguel; Sil, Avirup; Bowman, Sam; Hamza, Wael; Søgaard, Anders; Naseem, Tahira & Goldberg, Yoav (Red.),
Proceedings of the Workshop on the Relevance of Linguistic Structure in Neural Architectures for NLP.
Association for Computational Linguistics.
ISSN 978-1-948087-42-1.
doi:
10.18653/v1/w18-2907.
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Nooralahzadeh, Farhad; Øvrelid, Lilja & Lønning, Jan Tore
(2018).
Evaluation of Domain-specific Word Embeddings using Knowledge Resources.
I Calzolari, Nicoletta; Choukri, Khalid; Cieri, Christopher; Declerck, Thierry; Goggi, Sara; Hasida, Koiti; Isahara, Hitoshi; Maegaard, Bente; Mariani, Joseph; Mazo, Hélène; Moreno, Asuncion; Odijk, Jan; Piperidis, Stelios & Tokunaga, Takenobu (Red.),
Proceedings of the Eleventh International Conference on Language Resources and Evaluation.
European Language Resources Association.
ISSN 979-10-95546-00-9.
s. 1438–1445.
Vis sammendrag
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.
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Øvrelid, Lilja; Kåsen, Andre; Hagen, Kristin; Solberg, Per Erik; Johannessen, Janne Bondi & Nøklestad, Anders
(2018).
The LIA Treebank of Spoken Norwegian Dialects.
I Calzolari, Nicoletta; Choukri, Khalid; Cieri, Christopher; Declerck, Thierry; Goggi, Sara; Hasida, Koiti; Isahara, Hitoshi; Maegaard, Bente; Mariani, Joseph; Mazo, Hélène; Moreno, Asuncion; Odijk, Jan; Piperidis, Stelios & Tokunaga, Takenobu (Red.),
Proceedings of the Eleventh International Conference on Language Resources and Evaluation.
European Language Resources Association.
ISSN 979-10-95546-00-9.
s. 4482–4488.
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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.
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Velldal, Erik; Øvrelid, Lilja; Bergem, Eivind Alexander; Stadsnes, Cathrine; Touileb, Samia & Jørgensen, Fredrik
(2018).
NoReC: The Norwegian Review Corpus.
I Calzolari, Nicoletta; Choukri, Khalid; Cieri, Christopher; Declerck, Thierry; Goggi, Sara; Hasida, Koiti; Isahara, Hitoshi; Maegaard, Bente; Mariani, Joseph; Mazo, Hélène; Moreno, Asuncion; Odijk, Jan; Piperidis, Stelios & Tokunaga, Takenobu (Red.),
Proceedings of the Eleventh International Conference on Language Resources and Evaluation.
European Language Resources Association.
ISSN 979-10-95546-00-9.
s. 4186–4191.
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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.
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Lapponi, Emanuele; Oepen, Stephan & Øvrelid, Lilja
(2017).
EPE 2017: The Sherlock Negation Resolution Downstream Application.
I Oepen, Stephan (Red.),
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.
ISSN 978-1-945626-74-6.
s. 25–30.
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Oepen, Stephan; Øvrelid, Lilja; Björne, Jari; Johansson, Richard; Lapponi, Emanuele & Ginter, Filip
[Vis alle 7 forfattere av denne artikkelen]
(2017).
The 2017 Shared Task on Extrinsic Parser Evaluation: Towards a Reusable Community Infrastructure.
I Oepen, Stephan (Red.),
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.
ISSN 978-1-945626-74-6.
s. 1–16.
doi:
10.18653/v1/k18-2002.
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Kutuzov, Andrei; Velldal, Erik & Øvrelid, Lilja
(2017).
Temporal dynamics of semantic relations in word embeddings: an application to predicting armed conflict participants,
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing.
Association for Computational Linguistics.
ISSN 978-1-945626-83-8.
s. 1825–1830.
doi:
10.18653/v1/D17-1194.
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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.
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Kutuzov, Andrei; Velldal, Erik & Øvrelid, Lilja
(2017).
Tracing armed conflicts with diachronic word embedding models.
I Caselli, Tommaso (Red.),
Proceedings of the Events and Stories in the News Workshop.
Association for Computational Linguistics.
ISSN 978-1-945626-63-0.
s. 31–36.
doi:
10.18653/v1/W17-2705.
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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.
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Enger, Martine; Velldal, Erik & Øvrelid, Lilja
(2017).
An open-source tool for negation detection: a maximum-margin approach.
I Blanco, Eduardo; Morante, Roser & Saurí, Roser (Red.),
Proceedings of the Workshop on Computational Semantics Beyond Events and Roles.
Association for Computational Linguistics.
ISSN 978-1-945626-49-4.
s. 64–69.
doi:
10.18653/v1/w17-1810.
Vis sammendrag
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.
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Sand, Heidi Marion; Velldal, Erik & Øvrelid, Lilja
(2017).
Wordnet extension via word embeddings: Experiments on the Norwegian Wordnet.
I Tiedemann, Jörg (Red.),
Proceedings of the 21st Nordic Conference on Computational Linguistics (NoDaLiDa).
Linköping University Electronic Press.
ISSN 978-91-7685-601-7.
s. 298–302.
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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.
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Hohle, Petter; Øvrelid, Lilja & Velldal, Erik
(2017).
Optimizing a PoS Tagset for Norwegian Dependency Parsing.
I Tiedemann, Jörg (Red.),
Proceedings of the 21st Nordic Conference on Computational Linguistics (NoDaLiDa).
Linköping University Electronic Press.
ISSN 978-91-7685-601-7.
s. 142–151.
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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.
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Velldal, Erik; Øvrelid, Lilja & Hohle, Petter
(2017).
Joint UD Parsing of Norwegian Bokmål and Nynorsk.
I Tiedemann, Jörg (Red.),
Proceedings of the 21st Nordic Conference on Computational Linguistics (NoDaLiDa).
Linköping University Electronic Press.
ISSN 978-91-7685-601-7.
s. 1–10.
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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.
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Oepen, Stephan; Read, Jonathon; Scheffler, Tatjana; Sidarenka, Uladzimir; Stede, Manfred & Velldal, Erik
[Vis alle 7 forfattere av denne artikkelen]
(2016).
OPT: Oslo–Potsdam–Teesside. Pipelining Rules, Rankers, and Classifier Ensembles for Shallow Discourse Parsing.
I Xue, Nianwen (Red.),
Proceedings of the CoNLL-16 Shared Task.
Association for Computational Linguistics.
ISSN 978-1-932432-66-4.
s. 20–26.
doi:
10.18653/v1/k16-2002.
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Lien, Jostein; Velldal, Erik & Øvrelid, Lilja
(2015).
Improving cross-domain dependency parsing with dependency-derived clusters.
I Megyesi, Beáta (Red.),
Proceedings of the 20th Nordic Conference of Computational Linguistics.
Linköping University Electronic Press.
ISSN 978-91-7519-098-3.
s. 117–126.
Fulltekst i vitenarkiv
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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.
I Balahur, Alexandra (Red.),
5th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis.
Association for Computational Linguistics.
ISSN 978-1-634392-06-8.
s. 90–96.
doi:
10.3115/v1/w14-2616.
Fulltekst i vitenarkiv
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Solberg, Per Erik; Skjærholt, Arne; Øvrelid, Lilja; Hagen, Kristin & Johannessen, Janne Bondi
(2014).
The Norwegian Dependency Treebank.
I Calzolari, Nicoletta; Choukri, Khalid; Declerck, Thierry; Loftsson, Hrafn; Maegaard, Bente; Mariani, Joseph; Moreno, Asuncion; Odijk, Jan & Piperidis, Stelios (Red.),
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14).
European Language Resources Association.
ISSN 978-2-9517408-8-4.
s. 789–795.
Fulltekst i vitenarkiv
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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.
I Bunt, Harry; Sima’an, Khalil & Huang, Liang (Red.),
Proceedings of The 13th International Conference on Parsing Technologies IWPT-2013.
Association for Computational Linguistics.
ISSN 978-1-937284-76-3.
Fulltekst i vitenarkiv
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Ivanova, Angelina; Oepen, Stephan & Øvrelid, Lilja
(2013).
Survey on parsing three dependency representations for English.
I Dey, Anik; Krause, Sebastian; Nikolova, Ivelina; Vecchi, Eva; Bethard, Steven; Nakov, Preslav I. & Xu, Feiyu (Red.),
51st Annual Meeting of the Association for Computational Linguistics Proceedings of the Student Research Workshop.
Association for Computational Linguistics.
ISSN 978-1-937284-53-4.
s. 31–37.
Vis sammendrag
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.
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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.
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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.
Vis sammendrag
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.
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Lapponi, Emanuele; Velldal, Erik; Øvrelid, Lilja & Read, Jonathon
(2012).
UiO2: Sequence-labeling Negation Using Dependency Features.
I Agirre, Eneko; Bos, Johan & Diab, Mona (Red.),
*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.
ISSN 978-1-937284-21-3.
s. 319–327.
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Read, Jonathon; Flickinger, Dan; Dridan, Rebecca; Oepen, Stephan & Øvrelid, Lilja
(2012).
The WeSearch Corpus, Treebank and Treecache: A Comprehensive Sample of User-Generated Content.
I Calzolari, Nicoletta; Choukri, Khalid; Declerck, Thierry; Ugur Dogan, Mehmet; Maegaard, Bente; Mariani, Joseph & Odijk, Jan (Red.),
Proceedings of the Eighth International Conference on Language Resources and Evaluation.
European Language Resources Association.
ISSN 978-2-9517408-7-7.
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Touileb, Samia; Øvrelid, Lilja & Velldal, Erik
(2021).
Using Gender- and Polarity-informed Models to Investigate Bias.
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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.
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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.
Vis sammendrag
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.
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Pilán, Ildikó; Brekke, Pål H. & Øvrelid, Lilja
(2020).
Building a Norwegian Lexical Resource for Medical Entity Recognition.
Vis sammendrag
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.
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Øvrelid, Lilja
(2019).
Forskjeller mellom talemål og skrift. Hva kan trebanker fortelle oss?
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Dahl, Fredrik Andreas; Kasicheyanula, Taraka Rama; Hurlen, Petter; Brekke, Pål Haugar; Husby, Haldor & Gundersen, Tore
[Vis alle 8 forfattere av denne artikkelen]
(2019).
Classifying Norwegian radiology reports with deep learning.
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Øvrelid, Lilja
(2012).
Constituent-based discriminative ranking for negation resolution.
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Publisert
4. nov. 2010 14:26
- Sist endret
26. nov. 2019 17:05