Our latest publications - LTG

• 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 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). 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.
• Ustalov, Dmitry; Panchenko, Alexander; Kutuzov, Andrei; Biemann, Chris & Ponzetto, Simone (2018). Unsupervised Semantic Frame Induction using Triclustering, In  Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers).  Association for Computational Linguistics.  ISBN 978-1-948087-34-6.  conference paper.  s 55 - 62 Show summary

  We use dependency triples automatically extracted from a Web-scale corpus to perform unsupervised semantic frame induction. We cast the frame induction problem as a triclustering problem that is a generalization of clustering for triadic data. Our replicable benchmarks demonstrate that the proposed graph-based approach, Triframes, shows state-of-the art results on this task on a FrameNet-derived dataset and performing on par with competitive methods on a verb class clustering task.

• Kutuzov, Andrei (2018). Russian Word Sense Induction by Clustering Averaged Word Embeddings. Komp'yuternaya Lingvistika i Intellektual'nye Tekhnologii.  ISSN 2221-7932.  (17), s 391- 403 Show summary

  The paper reports our participation in the shared task on word sense induction and disambiguation for the Russian language (RUSSE’2018). Our team was ranked 2nd for the wiki-wiki dataset (containing mostly homonyms) and 5th for the bts-rnc and active-dict datasets (containing mostly polysemous words) among all 19 participants. The method we employed was extremely naive. It implied representing contexts of ambiguous words as averaged word embedding vectors, using off-the-shelf pre-trained distributional models. Then, these vector representations were clustered with mainstream clustering techniques, thus producing the groups corresponding to the ambiguous word’ senses. As a side result, we show that word embedding models trained on small but balanced corpora can be superior to those trained on large but noisy data—not only in intrinsic evaluation, but also in downstream tasks like word sense induction.

• 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
• 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
• Øvrelid, Lilja; Kåsen, Andre; Hagen, Kristin; Nøklestad, Anders; Solberg, Per Erik & Johannessen, Janne Bondi (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.  s 4482 - 4488 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.

• 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.

• Lapponi, Emanuele; Søyland, Martin G.; Velldal, Erik & Oepen, Stephan (2018). The Talk of Norway: A Richly Annotated Corpus of the Norwegian Parliament, 1998–2016. Language Resources and Evaluation.  ISSN 1574-020X. . doi: 10.1007/s10579-018-9411-5 Show summary

  In this work we present the Talk of Norway (ToN) data set, a collection of Norwegian Parliament speeches from 1998 to 2016. Every speech is richly annotated with metadata harvested from different sources, and augmented with language type, sentence, token, lemma, part-of-speech, and morphological feature annotations. We also present a pilot study on party classification in the Norwegian Parliament, carried out in the context of a cross-faculty collaboration involving researchers from both Political Science and Computer Science. Our initial experiments demonstrate how the linguistic and institutional annotations in ToN can be used to gather insights on how different aspects of the political process affect classification.

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• Nooralahzadeh, Farhad & Øvrelid, Lilja (2018). Syntactic Dependency Representations in Neural Relation Classification.

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