Here are some of the courses I have taught (last time in 2022):

Corpus-based Methods, LIM024, 7.5 ECTS credits [in English]
Mathematical Methods for Linguists, LIN433, 7.5 ECTS credits [in Swedish]
Thesis courses for the Degree of Bachelor, LIN612/LIN622/LIN633/LIN640/LIT330, 15 ECTS credits [in Swedish]

 

A selection from Stockholm University publication database

• Annotating the Narrative: A Plot of Scenes, Events, Characters and Other Intriguing Elements

  2022. Mats Wirén, Adam Ek, Murathan Kurfalı. LIVE and LEARN, 161-164

  Chapter

  Analysis of narrative structure in prose fiction is a field which is gaining increased attention in NLP, and which potentially has many interesting and more far-reaching applications. This paper provides a summary and motivation of two different but interrelated strands of work that we have carried out in this field during the last years: on the one hand, principles and guidelines for annotation, and on the other, methods for automatic annotation. 

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• Breaking the Narrative: Scene Segmentation through Sequential Sentence Classification

  2021. Murathan Kurfalı, Mats Wirén.

  Conference

  In this paper, we describe our submission to the Shared Task on Scene Segmentation (STSS). The shared task requires participants to segment novels into coherent segments, called scenes. We approach this as a sequential sentence classification task and offer a BERT-based solution with a weighted cross-entropy loss. According to the results, the proposed approach performs relatively well on the task as our model ranks first and second, in official in-domain and out-domain evaluations, respectively. However, the overall low performances (0.37 F1-score) suggest that there is still much room for improvement.

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• Annotation Guideline No. 7 (revised): Guidelines for annotation of narrative structure

  2021. Mats Wirén, Adam Ek. Journal of Cultural Analytics 6 (4), 164-186

  Article

  Analysis of narrative structure can be said to answer the question “Who tells what, and how?”. The key part of our annotation scheme is related to the “who?”, and to this end we distinguish between narration and fictional dialogue. Furthermore, with respect to the latter we keep track of turns, lines, identities of speakers and addressees, and speech-framing constructions, which provide the narrator’s cues about the circumstances of the speech. We also annotate voice, that is, whether the narrator is ever present in the story or not. Our annotation of the “what?” includes embeddings of narrative transmission levels to capture stories in stories, and embeddings of fictional dialogue to capture characters quoting other characters. Our annotation of the “how?” includes focalization, that is, the perspective from which the narrative is seen and how much information the narrator has access to.

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• Zero-shot cross-lingual identification of direct speech using distant supervision

  2020. Murathan Kurfali, Mats Wirén. The 4th Joint SIGHUM Workshopon Computational Linguistics for Cultural Heritage,Social Sciences, Humanities and Literature, 105-111

  Conference

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• SVALA: Annotation of Second-Language Learner Text Based on Mostly Automatic Alignment of Parallel Corpora

  2019. Mats Wirén (et al.). Selected papers from the CLARIN Annual Conference 2018, Pisa, 8-10 October 2018, 222-234

  Conference

  Annotation of second-language learner text is a cumbersome manual task which in turn requires interpretation to postulate the intended meaning of the learner’s language. This paper describes SVALA, a tool which separates the logical steps in this process while providing rich visual support for each of them. The first step is to pseudonymize the learner text to fulfil the legal and ethical requirements for a distributable learner corpus. The second step is to correct the text, which is carried out in the simplest possible way by text editing. During the editing, SVALA automatically maintains a parallel corpus with alignments between words in the learner source text and corrected text, while the annotator may repair inconsistent word alignments. Finally, the actual labelling of the corrections (the postulated errors) is performed. We describe the objectives, design and workflow of SVALA, and our plans for further development.

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Show all publications by Mats Wirén at Stockholm University