/ Introduction
Language technologies are at the core of media technologies. This work package aims to provide datasets and models for Norwegian (Bokmål/Nynorsk) that support the automated understanding as well as the automated production of media texts in this language.
Objective: WP5 adopts theoretical approaches and methodologies primarily based on linguistic data science, including neural learning. Based on language data in the media from the user partners and data and tools at the research partners, large corpora will be annotated. The labelled examples in these corpora will be used for training and evaluating supervised models that demonstrate advanced approaches in areas such as robust deep language analysis, adaptive language generation, event identification and extraction, and analyzing opinions. The partners will cooperate to explore the use of such models for innovative purposes.
/ Introduction
Language technologies are at the core of media technologies. This work package aims to provide datasets and models for Norwegian (Bokmål/Nynorsk) that support the automated understanding as well as the automated production of media texts in this language.
Objective: WP5 adopts theoretical approaches and methodologies primarily based on linguistic data science, including neural learning. Based on language data in the media from the user partners and data and tools at the research partners, large corpora will be annotated. The labelled examples in these corpora will be used for training and evaluating supervised models that demonstrate advanced approaches in areas such as robust deep language analysis, adaptive language generation, event identification and extraction, and analyzing opinions. The partners will cooperate to explore the use of such models for innovative purposes.
/ Introduction
Language technologies are at the core of media technologies. This work package aims to provide datasets and models for Norwegian (Bokmål/Nynorsk) that support the automated understanding as well as the automated production of media texts in this language.
Objective: WP5 adopts theoretical approaches and methodologies primarily based on linguistic data science, including neural learning. Based on language data in the media from the user partners and data and tools at the research partners, large corpora will be annotated. The labelled examples in these corpora will be used for training and evaluating supervised models that demonstrate advanced approaches in areas such as robust deep language analysis, adaptive language generation, event identification and extraction, and analyzing opinions. The partners will cooperate to explore the use of such models for innovative purposes.
/ People






/ Publications
2020
Gender and sentiment, critics and authors: a dataset of Norwegian book reviews Journal Article
In: Gender Bias in Natural Language Processing. Association for Computational Linguistics, 2020, (Pre SFI).
Improving sentiment analysis with multi-task learning of negation Journal Article
In: 2020, (Pre SFI).
Sentiment analysis is not solved! Assessing and probing sentiment classification Proceeding
2020, (Pre SFI).
Identifying Sentiments in Algerian Code-switched User-generated Comments Conference
2020, (Pre SFI).
Interactive Visualizations in INESS Book Chapter
In: Butt, M.; Hautli-Janisz, A.; (Eds.), V. Lyding (Ed.): 2020, (Pre SFI).
NorNE: Annotating Named Entities for Norwegian Proceeding
2020, (Pre SFI).
A Fine-Grained Sentiment Dataset for Norwegian Proceeding
2020, (Pre SFI).
Named Entity Recognition without Labelled Data: A Weak Supervision Approach Journal Article
In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 1518–1533, 2020, (Pre SFI).
FAIR Digital Objects for Science: From Data Pieces to Actionable Knowledge Units Journal Article
In: 2020, (Pre SFI).
2019
Lexicon information in neural sentiment analysis: a multi-task learning approach Conference
Linköping University Electronic Press, 2019, (Pre SFI).
2018
Diachronic word embeddings and semantic shifts: a survey Proceeding
2018, (Pre SFI).
NoReC: The Norwegian Review Corpus Proceeding
2018, (Pre SFI).
2017
Automatic identification of unknown names with specific roles Journal Article
In: Proceedings of the Second Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, pp. 150-158, 2017, (Pre SFI).
Word vectors, reuse, and replicability: Towards a community repository of large-text resources Proceeding
2017, (Pre SFI).
2016
The enrichment of lexical resources through incremental parsebanking Journal Article
In: 2016, (Pre SFI).
NorGramBank: A 'Deep' Treebank for Norwegian.Proceedings of LREC Proceeding
2016, (Pre SFI).
MWEs in Treebanks: From Survey to Guidelines Proceeding
2016, (Pre SFI).
Universal dependencies for Norwegian Proceeding
2016, (Pre SFI).
2012
Representing and resolving negation for sentiment analysis Proceeding
2012, (Pre SFI).
Speculation and negation: Rules, rankers, and the role of syntax Journal Article
In: 2012, (Pre SFI).
/ Publications
2020
Gender and sentiment, critics and authors: a dataset of Norwegian book reviews Journal Article
In: Gender Bias in Natural Language Processing. Association for Computational Linguistics, 2020, (Pre SFI).
Improving sentiment analysis with multi-task learning of negation Journal Article
In: 2020, (Pre SFI).
Sentiment analysis is not solved! Assessing and probing sentiment classification Proceeding
2020, (Pre SFI).
Identifying Sentiments in Algerian Code-switched User-generated Comments Conference
2020, (Pre SFI).
Interactive Visualizations in INESS Book Chapter
In: Butt, M.; Hautli-Janisz, A.; (Eds.), V. Lyding (Ed.): 2020, (Pre SFI).
NorNE: Annotating Named Entities for Norwegian Proceeding
2020, (Pre SFI).
A Fine-Grained Sentiment Dataset for Norwegian Proceeding
2020, (Pre SFI).
Named Entity Recognition without Labelled Data: A Weak Supervision Approach Journal Article
In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 1518–1533, 2020, (Pre SFI).
FAIR Digital Objects for Science: From Data Pieces to Actionable Knowledge Units Journal Article
In: 2020, (Pre SFI).
2019
Lexicon information in neural sentiment analysis: a multi-task learning approach Conference
Linköping University Electronic Press, 2019, (Pre SFI).
2018
Diachronic word embeddings and semantic shifts: a survey Proceeding
2018, (Pre SFI).
NoReC: The Norwegian Review Corpus Proceeding
2018, (Pre SFI).
2017
Automatic identification of unknown names with specific roles Journal Article
In: Proceedings of the Second Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, pp. 150-158, 2017, (Pre SFI).
Word vectors, reuse, and replicability: Towards a community repository of large-text resources Proceeding
2017, (Pre SFI).
2016
The enrichment of lexical resources through incremental parsebanking Journal Article
In: 2016, (Pre SFI).
NorGramBank: A 'Deep' Treebank for Norwegian.Proceedings of LREC Proceeding
2016, (Pre SFI).
MWEs in Treebanks: From Survey to Guidelines Proceeding
2016, (Pre SFI).
Universal dependencies for Norwegian Proceeding
2016, (Pre SFI).
2012
Representing and resolving negation for sentiment analysis Proceeding
2012, (Pre SFI).
Speculation and negation: Rules, rankers, and the role of syntax Journal Article
In: 2012, (Pre SFI).
/ Publications
2020
Gender and sentiment, critics and authors: a dataset of Norwegian book reviews Journal Article
In: Gender Bias in Natural Language Processing. Association for Computational Linguistics, 2020, (Pre SFI).
Improving sentiment analysis with multi-task learning of negation Journal Article
In: 2020, (Pre SFI).
Sentiment analysis is not solved! Assessing and probing sentiment classification Proceeding
2020, (Pre SFI).
Identifying Sentiments in Algerian Code-switched User-generated Comments Conference
2020, (Pre SFI).
Interactive Visualizations in INESS Book Chapter
In: Butt, M.; Hautli-Janisz, A.; (Eds.), V. Lyding (Ed.): 2020, (Pre SFI).
NorNE: Annotating Named Entities for Norwegian Proceeding
2020, (Pre SFI).
A Fine-Grained Sentiment Dataset for Norwegian Proceeding
2020, (Pre SFI).
Named Entity Recognition without Labelled Data: A Weak Supervision Approach Journal Article
In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 1518–1533, 2020, (Pre SFI).
FAIR Digital Objects for Science: From Data Pieces to Actionable Knowledge Units Journal Article
In: 2020, (Pre SFI).
2019
Lexicon information in neural sentiment analysis: a multi-task learning approach Conference
Linköping University Electronic Press, 2019, (Pre SFI).
2018
Diachronic word embeddings and semantic shifts: a survey Proceeding
2018, (Pre SFI).
NoReC: The Norwegian Review Corpus Proceeding
2018, (Pre SFI).
2017
Automatic identification of unknown names with specific roles Journal Article
In: Proceedings of the Second Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, pp. 150-158, 2017, (Pre SFI).
Word vectors, reuse, and replicability: Towards a community repository of large-text resources Proceeding
2017, (Pre SFI).
2016
The enrichment of lexical resources through incremental parsebanking Journal Article
In: 2016, (Pre SFI).
NorGramBank: A 'Deep' Treebank for Norwegian.Proceedings of LREC Proceeding
2016, (Pre SFI).
MWEs in Treebanks: From Survey to Guidelines Proceeding
2016, (Pre SFI).
Universal dependencies for Norwegian Proceeding
2016, (Pre SFI).
2012
Representing and resolving negation for sentiment analysis Proceeding
2012, (Pre SFI).
Speculation and negation: Rules, rankers, and the role of syntax Journal Article
In: 2012, (Pre SFI).