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  • 1.
    Boye, Johan
    et al.
    TeliaSonera R and D.
    Wirén, M.
    Robust parsing and spoken negotiative dialogue with databases2008In: Natural Language Engineering, ISSN 1351-3249, E-ISSN 1469-8110, Vol. 14, no 3, p. 289-312Article in journal (Refereed)
    Abstract [en]

    This paper presents a robust parsing algorithm and semantic formalism for the interpretation of utterances in spoken negotiative dialogue with databases. The algorithm works in two passes: a domain-specific pattern-matching phase and a domain-independent semantic analysis phase. Robustness is achieved by limiting the set of representable utterance types to an empirically motivated subclass which is more expressive than propositional slot–value lists, but much less expressive than first-order logic. Our evaluation shows that in actual practice the vast majority of utterances that occur can be handled, and that the parsing algorithm is highly efficient and accurate.

  • 2.
    Carlberger, Johan
    et al.
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    Domeij, Rickard
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    Kann, Viggo
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    Knutsson, Ola
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    The development and performance of a grammar checker for Swedish: A language engineering perspective2004In: Natural Language Engineering, ISSN 1351-3249, E-ISSN 1469-8110, Vol. 1, no 1Article in journal (Refereed)
    Abstract [en]

    This article describes the construction and performance of Granska – a surface-oriented system for grammar checking of Swedish text. With the use of carefully constructed error detection rules, written in a new structured rule language, the system can detect and suggest corrections for a number of grammatical errors in Swedish texts. In this article, we specifically focus on how erroneously split compounds and disagreement are handled in the rules. The system combines probabilistic and rule-based methods to achieve high efficiency and robustness. The error detection rules are optimized using statistics of part-of-speech bigrams and words in a way that each rule needs to be checked as seldom as possible. We have found that the Granska system with higher efficiency can achieve the same or better results than systems with conventional technology.

  • 3.
    Gustafson, Joakim
    et al.
    KTH, Superseded Departments, Speech, Music and Hearing.
    Bell, Linda
    KTH, Superseded Departments, Speech, Music and Hearing.
    Speech technology on trial: Experiences from the August system2000In: Natural Language Engineering, ISSN 1351-3249, E-ISSN 1469-8110, Vol. 6, no 3-4, p. 273-286Article in journal (Refereed)
    Abstract [en]

    In this paper, the August spoken dialogue system is described. This experimental Swedish dialogue system, which featured an animated talking agent, was exposed to the general public during a trial period of six months. The construction of the system was partly motivated by the need to collect genuine speech data from people with little or no previous experience of spoken dialogue systems. A corpus of more than 10,000 utterances of spontaneous computer- directed speech was collected and empirical linguistic analyses were carried out. Acoustical, lexical and syntactical aspects of this data were examined. In particular, user behavior and user adaptation during error resolution were emphasized. Repetitive sequences in the database were analyzed in detail. Results suggest that computer-directed speech during error resolution is increased in duration, hyperarticulated and contains inserted pauses. Design decisions which may have influenced how the users behaved when they interacted with August are discussed and implications for the development of future systems are outlined.

  • 4.
    Karlgren, Jussi
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Theoretical Computer Science, TCS.
    Kanerva, P.
    High-dimensional distributed semantic spaces for utterances2019In: Natural Language Engineering, ISSN 1351-3249, E-ISSN 1469-8110, Vol. 25, no 4, p. 503-517Article in journal (Refereed)
    Abstract [en]

    High-dimensional distributed semantic spaces have proven useful and effective for aggregating and processing visual, auditory and lexical information for many tasks related to human-generated data. Human language makes use of a large and varying number of features, lexical and constructional items as well as contextual and discourse-specific data of various types, which all interact to represent various aspects of communicative information. Some of these features are mostly local and useful for the organisation of, for example, argument structure of a predication; others are persistent over the course of a discourse and necessary for achieving a reasonable level of understanding of the content. This paper describes a model for high-dimensional representation for utterance and text-level data including features such as constructions or contextual data, based on a mathematically principled and behaviourally plausible approach to representing linguistic information. The implementation of the representation is a straightforward extension of Random Indexing models previously used for lexical linguistic items. The paper shows how the implementedmodel is able to represent a broad range of linguistic features in a common integral framework of fixed dimensionality, which is computationally habitable, and which is suitable as a bridge between symbolic representations such as dependency analysis and continuous representations used, for example, in classifiers or further machine-learning approaches. This is achieved with operations on vectors that constitute a powerful computational algebra, accompanied with an associative memory for the vectors. The paper provides a technical overview of the framework and a worked through implemented example of how it can be applied to various types of linguistic features.

  • 5. Sahlgren, Magnus
    et al.
    Karlgren, Jussi
    Swedish Institute of Computer Science, Sweden.
    Automatic Bilingual Lexicon Acquisition Using Random Indexing of Parallel Corpora2005In: Natural Language Engineering, ISSN 1351-3249, E-ISSN 1469-8110, Vol. 11, no 3, p. 327-341Article in journal (Refereed)
    Abstract [en]

    This paper presents a very simple and effective approach to using parallel corpora for automatic bilingual lexicon acquisition. The approach, which uses the Random Indexing vector space methodology, is based on finding correlations between terms based on their distributional characteristics. The approach requires a minimum of preprocessing and linguistic knowledge, and is efficient, fast and scalable. In this paper, we explain how our approach differs from traditional cooccurrence-based word alignment algorithms, and we demonstrate how to extract bilingual lexica using the Random Indexing approach applied to aligned parallel data. The acquired lexica are evaluated by comparing them to manually compiled gold standards, and we report overlap of around 60%. We also discuss methodological problems with evaluating lexical resources of this kind.

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