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  • 1.
    Börstell, Carl
    et al.
    Stockholm University, Faculty of Humanities, Department of Linguistics, Sign Language.
    Östling, Robert
    Stockholm University, Faculty of Humanities, Department of Linguistics, Computational Linguistics.
    Iconic Locations in Swedish Sign Language: Mapping Form to Meaning with Lexical Databases2017In: Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa / [ed] Jörg Tiedemann, Linköping: Linköping University Electronic Press, 2017, p. 221-225, article id 026Conference paper (Refereed)
    Abstract [en]

    In this paper, we describe a method for mapping the phonological feature location of Swedish Sign Language (SSL) signs to the meanings in the Swedish semantic dictionary SALDO. By doing so, we observe clear differences in the distribution of meanings associated with different locations on the body. The prominence of certain locations for specific meanings clearly point to iconic mappings between form and meaning in the lexicon of SSL, which pinpoints modalityspecific properties of the visual modality.

  • 2.
    Cap, Fabienne
    et al.
    Uppsala University.
    Adesam, Yvonne
    University of Gothenburg.
    Ahrenberg, Lars
    Linköping University.
    Borin, Lars
    University of Gothenburg.
    Bouma, Gerlof
    University of Gothenburg.
    Forsberg, Markus
    University of Gothenburg.
    Kann, Viggo
    KTH.
    Östling, Robert
    Stockholm University, Faculty of Humanities, Department of Linguistics, Computational Linguistics.
    Smith, Aaron
    Uppsala University.
    Wirén, Mats
    Stockholm University, Faculty of Humanities, Department of Linguistics, Computational Linguistics.
    Nivre, Joakim
    Uppsala University.
    SWORD: Towards Cutting-Edge Swedish Word Processing2016In: SLTC 2016: The Sixth Swedish Language Technology Conference (SLTC), SLTC , 2016, Vol. 6Conference paper (Refereed)
    Abstract [en]

    Despite many years of research on Swedish language technology, there is still no well-documented standard for Swedish word processing covering the whole spectrum from low-level tokenization to morphological analysis and disambiguation. SWORD is a new initiative within the SWE-CLARIN consortium aiming to develop documented standards for Swedish word processing. In this paper, we report on a pilot study of Swedish tokenization, where we compare the output of six different tokenizers on four different text types. For one text type (Wikipedia articles), we also compare to the tokenization produced by six manual annotators.

  • 3.
    Eriksson, Anders
    et al.
    Stockholm University, Faculty of Humanities, Department of Linguistics. Fonetik.
    Lacerda, Francisco
    Stockholm University, Faculty of Humanities, Department of Linguistics. Fonetik.
    Charlatanry in forensic speech science: A problem to be taken seriously2007In: International Journal of Speech, Language and the Law: (formerly Forensic Linguistics: ISSN 1350-1771), ISSN 1748-8885, Vol. 14, no 2, p. 169-193Article in journal (Refereed)
    Abstract [en]

    A lie detector which can reveal lie and deception in some automatic and perfectly reliable way is an old idea we have often met with in science fiction books and comic strips. This is all very well. It is when machines claimed to be lie detectors appear in the context of criminal investigations or security applications that we need to be concerned. In the present paper we will describe two types of ‘deception’ or ‘stress detectors’ (euphemisms to refer to what quite clearly are known as ‘lie detectors’). Both types of detection are claimed to be based on voice analysis but we found no scientific evidence to support the manufacturers’ claims. Indeed, our review of scientific studies will show that these machines perform at chance level when tested for reliability. Given such results and the absence of scientific support for the underlying principles it is justified to view the use of these machines as charlatanry and we argue that there are serious ethical and security reasons to demand that responsible authorities and institutions should not get involved in such practices.

  • 4.
    Grigonyté, Gintaré
    et al.
    Stockholm University, Faculty of Humanities, Department of Linguistics, Computational Linguistics.
    Nilsson Björkenstam, Kristina
    Stockholm University, Faculty of Humanities, Department of Linguistics, Computational Linguistics.
    Language-independent exploration of repetition and variation in longitudinal child-directed speech: A tool and resources2016In: Proceedings of the joint workshop on NLP for Computer Assisted Language Learning and NLP for Language Acquisition at SLTC, Umeå, 16th November 2016 / [ed] Elena Volodina, Gintarė Grigonytė, Ildikó Pilán, Kristina Nilsson Björkenstam, Lars Borin, Linköping: Linköping University Electronic Press, 2016, p. 41-50Conference paper (Refereed)
    Abstract [en]

    We present a language-independent tool, called Varseta, for extracting variation sets in child-directed speech. This tool is evaluated against a gold standard corpus annotated with variation sets, MINGLE-3-VS, and used to explore variation sets in 26 languages in CHILDES-26-VS, a comparable corpus derived from the CHILDES database. The tool and the resources are freely available for re-search.

  • 5.
    Lindberg, Inger
    et al.
    Stockholm University, Faculty of Humanities, Department of Education in Languages and Language Development.
    Johansson Kokkinakis, Sofie
    Word Type Grouping in Secondary School Textbooks.2008In: Proceedings on TaLC 8, the 8th Teaching and Language Corpora Conference, Lisbon, 2008, p. 4-Conference paper (Refereed)
    Abstract [en]

    According to second language research (Saville-Troike 1984), vocabulary size is the single most determinant factor for second language students in order to be successful in a school setting. This has to do with the close relationship between reading comprehension and vocabulary knowledge (Read 2000). The persistent gap between reading performance of first and second language students observed in many studies (Taube 2002) is thus intimately related to low vocabulary among second language students. According to some estimates, differences in vocabulary size between first and second language students at school start may amount to several thousand words and tend to increase over the school years (Verhoeven & Vermeer 1985). According to some researchers there is a yearly increase of approximately 3000 words in the vocabulary size of school children in general (Viberg 1993). This means that many second language students face the task of trying to close a gap in vocabulary size of thousands of words while at the same time trying to keep up with the extensive vocabulary growth of first language students.

    But much could be done to make vocabulary instruction more systematic and efficient if we knew more about the vocabulary needs for successful learning in different subjects at school. Schoolbook texts constitute important data for finding answers to questions like What characterizes the vocabulary of schoolbook texts in general and in different subjects at different levels? and Which words present particular problems for students studying in their second language? To answer such and other questions related to school related vocabulary and second language learning we have compiled and analyzed a corpus of secondary school textbooks of one million words (OrdiL) with texts from eight different school subjects (Lindberg & Johansson Kokkinakis 2007). To identify and categorize various types of words in textbooks from a second language perspective, we propose a model based on earlier research by Coxhead & Nation (2001) and Hyland & Tse (2007) modified to account for all the word types of potential difficulty for second language secondary school students.

  • 6.
    Lindström, Eva
    Stockholm University, Faculty of Humanities, Department of Linguistics, General Linguistics.
    Some Uses of Demonstratives in Spoken Swedish2000In: Corpus-based and Computational Approaches to Discourse Anaphora / [ed] Botley, S.P. & McEnery, A.M., Amsterdam: John Benjamins Publishing Company, 2000, p. 107-128Chapter in book (Refereed)
    Abstract [en]

    This paper presents work in progress on some aspects of the use of one set of demonstrative expressions in a corpus of conversational Swedish. The demonstratives under study are the compound forms den här and den där (Eng. approx. ‘this’, ‘that’), both as pronouns and determiners. These forms belong mainly to the spoken language, and have not received much attention in previous studies of Swedish. Typical cases of deictic, first-mention and anaphoric uses are illustrated, and cases that cause problems for the distinction between first mention and anaphor are discussed. A surprisingly large number of first mentions with demonstratives were found, many of which are used in what is here called the “you know” function of demonstratives, i.e., a means for the speaker of signalling his or her assumption of the listener having a previous representation of the intended referent. Among anaphoric uses, some interesting occurrences are discussed, which resemble cases previously described as ‘identificationally overspecified’ (Maes and Noordman, 1995).

  • 7.
    Nilsson Björkenstam, Kristina
    Stockholm University, Faculty of Humanities, Department of Linguistics, Computational Linguistics.
    What is a corpus and why are corpora important tools?2013In: How can we use sign language corpora?: Papers from Nordic seminar ”How can we use sign language corpora?” 12–13 December 2013, Copenhagen, Denmark / [ed] Tommy Lyxell, Stockholm: Språkrådet , 2013Conference paper (Other academic)
  • 8.
    Nilsson Björkenstam, Kristina
    et al.
    Stockholm University, Faculty of Humanities, Department of Linguistics, Computational Linguistics.
    Grigonyté, Gintaré
    Stockholm University, Faculty of Humanities, Department of Linguistics, Computational Linguistics.
    Trump säger det igen, igen och igen2017In: Språktidningen, ISSN 1654-5028, no 2, p. 24-27Article in journal (Other (popular science, discussion, etc.))
  • 9.
    Nilsson Björkenstam, Kristina
    et al.
    Stockholm University, Faculty of Humanities, Department of Linguistics, Computational Linguistics.
    Wirén, Mats
    Stockholm University, Faculty of Humanities, Department of Linguistics, Computational Linguistics.
    Multimodal Annotation of Synchrony in Longitudinal Parent–Child Interaction2014In: MMC 2014 Multimodal Corpora: Combining applied and basic research targets: Workshop at LREC 2014, European Language Resources Association, 2014Conference paper (Refereed)
    Abstract [en]

    This paper describes the multimodal annotation of speech, gaze and hand movement in a corpus of longitudinal parent–child interaction,and reports results on synchrony, structural regularities which appear to be a key means for parents to facilitate learning of new conceptsto children. The results provide additional support for our previous finding that parents display decreasing synchrony as a function ofthe age of the child.

  • 10.
    Nilsson Björkenstam, Kristina
    et al.
    Stockholm University, Faculty of Humanities, Department of Linguistics, Computational Linguistics.
    Wirén, Mats
    Stockholm University, Faculty of Humanities, Department of Linguistics, Computational Linguistics.
    Reference to Objects in Longitudinal Parent-Child Interaction2012In: Workshop on Language, Action and Perception (APL), 2012Conference paper (Refereed)
    Abstract [en]

    A cognitive model of language learning needs to be dialogue-driven and multimodal to reflect how parent and child interact, using words, eye gaze, and object manipulation.

    In this paper, we present a scheme for multimodal annotation of parent-child interaction. We use this annotation for studying invariance across modalities. Our basic hypothesis is that perception of invariance (or synchrony) in multimodal patterns in auditory-visual speech is the device primarily used to reduce complexity in language learning.

    To this end, we have added verbal and non-verbal annotation to a corpus of longitudinal video and sound recordings of parent-child dyads. We use this data to try to determine if the amount of synchrony across modalities of parent-child interaction decreases as the child grows older and learns more language and gestures.

  • 11.
    Nilsson Björkenstam, Kristina
    et al.
    Stockholm University, Faculty of Humanities, Department of Linguistics, Computational Linguistics.
    Wirén, Mats
    Stockholm University, Faculty of Humanities, Department of Linguistics, Computational Linguistics.
    Variation sets in child-directed speech2015In: / [ed] Ellen Marklund, Iris-Corinna Schwarz, 2015Conference paper (Refereed)
  • 12.
    Nilsson Björkenstam, Kristina
    et al.
    Stockholm University, Faculty of Humanities, Department of Linguistics, Computational Linguistics.
    Wirén, Mats
    Stockholm University, Faculty of Humanities, Department of Linguistics, Computational Linguistics.
    Östling, Robert
    Stockholm University, Faculty of Humanities, Department of Linguistics, Computational Linguistics.
    Modelling the informativeness and timing of non-verbal cues in parent–child interaction2016In: The 54th Annual Meeting of the Association for Computational Linguistics: Proceedings of the 7th Workshop on Cognitive Aspects of Computational Language Learning, Stroudsburg, PA, USA: Association for Computational Linguistics, 2016, p. 82-90Conference paper (Refereed)
    Abstract [en]

    How do infants learn the meanings of their first words? This study investigates the informativeness and temporal dynamics of non-verbal cues that signal the speaker's referent in a model of early word–referent mapping. To measure the information provided by such cues, a supervised classifier is trained on information extracted from a multimodally annotated corpus of 18 videos of parent–child interaction with three children aged 7 to 33 months. Contradicting previous research, we find that gaze is the single most informative cue, and we show that this finding can be attributed to our fine-grained temporal annotation. We also find that offsetting the timing of the non-verbal cues reduces accuracy, especially if the offset is negative. This is in line with previous research, and suggests that synchrony between verbal and non-verbal cues is important if they are to be perceived as causally related.

  • 13.
    Nilsson Björkenstam, Kristina
    et al.
    Stockholm University, Faculty of Humanities, Department of Linguistics, Computational Linguistics.
    Östling, Robert
    Stockholm University, Faculty of Humanities, Department of Linguistics, Computational Linguistics.
    Wirén, Mats
    Stockholm University, Faculty of Humanities, Department of Linguistics, Computational Linguistics.
    Modelling the informativeness of different modalities in parent-child interaction2015In: Workshop on Extensive and Intensive Recordings of Children's Language Environment / [ed] Alex Cristia, Melanie Soderstrom, 2015Conference paper (Refereed)
  • 14.
    Schneider, Gerold
    et al.
    Institute of Computational Linguistics, University of Zurich, Switzerland.
    Grigonyté, Gintaré
    Stockholm University, Faculty of Humanities, Department of Linguistics, Computational Linguistics.
    Using an automatic parser as a language learner model2013Conference paper (Refereed)
  • 15.
    Sjons, Johan
    et al.
    Stockholm University, Faculty of Humanities, Department of Linguistics, Computational Linguistics.
    Hörberg, Thomas
    Stockholm University, Faculty of Humanities, Department of Linguistics, General Linguistics.
    Östling, Robert
    Stockholm University, Faculty of Humanities, Department of Linguistics, Computational Linguistics.
    Bjerva, Johannes
    Articulation rate in Swedish child-directed speech increases as a function of the age of the child even when surprisal is controlled for2017In: Proceedings of the 18th Annual Conference of the International Speech Communication Association (INTERSPEECH 2017) / [ed] Marcin Włodarczak, Stockholm: The International Speech Communication Association (ISCA), 2017, p. 1794-1798Conference paper (Refereed)
  • 16. Volodina, Elena
    et al.
    Grigonyté, GintaréStockholm University, Faculty of Humanities, Department of Linguistics, Computational Linguistics.Pilán, IldikóNilsson Björkenstam, KristinaStockholm University, Faculty of Humanities, Department of Linguistics, Computational Linguistics.Borin, Lars
    Proceedings of the joint workshop on NLP for Computer Assisted Language Learning and NLP for Language Acquisition at SLTC: Umeå 16th November 20162016Conference proceedings (editor) (Refereed)
  • 17.
    Wirén, Mats
    et al.
    Stockholm University, Faculty of Humanities, Department of Linguistics, Computational Linguistics.
    Nilsson Björkenstam, Kristina
    Stockholm University, Faculty of Humanities, Department of Linguistics, Computational Linguistics.
    Grigonytė, Gintarė
    Stockholm University, Faculty of Humanities, Department of Linguistics, Computational Linguistics.
    Cortes, Elisabet Eir
    Stockholm University, Faculty of Humanities, Department of Linguistics, Phonetics.
    Longitudinal Studies of Variation Sets in Child-directed Speech2016In: The 54th Annual Meeting of the Association for Computational Linguistics: Proceedings of the 7th Workshop on Cognitive Aspects of Computational Language Learning, Stroudsburg, PA, USA: Association for Computational Linguistics, 2016, p. 44-52Conference paper (Refereed)
    Abstract [en]

    One of the characteristics of child-directed speech is its high degree of repetitiousness. Sequences of repetitious utterances with a constant intention, variation sets, have been shown to be correlated with children’s language acquisition. To obtain a baseline for the occurrences of variation sets in Swedish, we annotate 18 parent–child dyads using a generalised definition according to which the varying form may pertain not just to the wording but also to prosody and/or non-verbal cues. To facilitate further empirical investigation, we introduce a surface algorithm for automatic extraction of variation sets which is easily replicable and language-independent. We evaluate the algorithm on the Swedish gold standard, and use it for extracting variation sets in Croatian, English and Russian. We show that the proportion of variation sets in child-directed speech decreases consistently as a function of children's age across Swedish, Croatian, English and Russian.

  • 18.
    Wirén, Mats
    et al.
    Stockholm University, Faculty of Humanities, Department of Linguistics, Computational Linguistics.
    Nilsson Björkenstam, Kristina
    Stockholm University, Faculty of Humanities, Department of Linguistics, Computational Linguistics.
    Östling, Robert
    Stockholm University, Faculty of Humanities, Department of Linguistics, Computational Linguistics.
    Modelling the Informativeness of Non-Verbal Cues in Parent–Child Interaction2017In: Proceedings of the 18th Annual Conference of the International Speech Communication Association (INTERSPEECH 2017), Stockholm: The International Speech Communication Association (ISCA), 2017, p. 2203-2207, article id 1143Conference paper (Refereed)
    Abstract [en]

    Non-verbal cues from speakers, such as eye gaze and hand positions, play an important role in word learning. This is consistent with the notion that for meaning to be reconstructed, acoustic patterns need to be linked to time-synchronous patterns from at least one other modality. In previous studies of a multimodally annotated corpus of parent–child interaction, we have shown that parents interacting with infants at the early word-learning stage (7–9 months) display a large amount of time-synchronous patterns, but that this behaviour tails off with increasing age of the children. Furthermore, we have attempted to quantify the informativeness of the different nonverbal cues, that is, to what extent they actually help to discriminate between different possible referents, and how critical the timing of the cues is. The purpose of this paper is to generalise our earlier model by quantifying informativeness resulting from non-verbal cues occurring both before and after their associated verbal references.

  • 19.
    Östling, Robert
    Stockholm University, Faculty of Humanities, Department of Linguistics, Computational Linguistics.
    Word order typology through multilingual word alignment2015In: The 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing: Proceedings of the Conference, Volume 2: Short Papers, 2015, p. 205-211Conference paper (Refereed)
    Abstract [en]

    With massively parallel corpora of hundreds or thousands of translations of the same text, it is possible to automatically perform typological studies of language structure using very large language samples. We investigate the domain of wordorder using multilingual word alignment and high-precision annotation transfer in a corpus with 1144 translations in 986 languages of the New Testament. Results are encouraging, with 86% to 96% agreementbetween our method and the manually created WALS database for a range of different word order features. Beyond reproducing the categorical data in WALS and extending it to hundreds of other languages, we also provide quantitative data for therelative frequencies of different word orders, and show the usefulness of this for language comparison. Our method has applications for basic research in linguistic typology, as well as for NLP tasks like transfer learning for dependency parsing, which has been shown to benefit from word order information.

  • 20.
    Östling, Robert
    et al.
    Stockholm University, Faculty of Humanities, Department of Linguistics, Computational Linguistics.
    Börstell, Carl
    Stockholm University, Faculty of Humanities, Department of Linguistics, General Linguistics.
    Wallin, Lars
    Stockholm University, Faculty of Humanities, Department of Linguistics, Sign Language.
    Enriching the Swedish Sign Language Corpus with Part of Speech Tags Using Joint Bayesian Word Alignment and Annotation Transfer2015In: Proceedings of the 20th Nordic Conference of Computational Linguistics: NODALIDA 2015, May 11-13, 2015, Vilnius, Lithuania / [ed] Beáta Megyesi, Linköping University Electronic Press, 2015, p. 263-268Conference paper (Refereed)
    Abstract [en]

    We have used a novel Bayesian model of joint word alignment and part of speech (PoS) annotation transfer to enrich the Swedish Sign Language Corpus with PoS tags. The annotations were then hand-corrected in order to both improve annotation quality for the corpus, and allow the empirical evaluation presented herein.

  • 21.
    Östling, Robert
    et al.
    Stockholm University, Faculty of Humanities, Department of Linguistics, Computational Linguistics.
    Wirén, Mats
    Stockholm University, Faculty of Humanities, Department of Linguistics, Computational Linguistics.
    Compounding in a Swedish Blog Corpus2013In: Computer mediated discourse across languages / [ed] Laura Álvarez López, Charlotta Seiler Brylla & Philip Shaw, Stockholm: Acta Universitatis Stockholmiensis, 2013, p. 45-63Chapter in book (Refereed)
1 - 21 of 21
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