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Harbusch, K., & Kempen, G. (2011). Automatic online writing support for L2 learners of German through output monitoring by a natural-language paraphrase generator. In M. Levy, F. Blin, C. Bradin Siskin, & O. Takeuchi (
Eds. ), WorldCALL: International perspectives on computer-assisted language learning (pp. 128-143). New York: Routledge.Abstract
Students who are learning to write in a foreign language, often want feedback on the grammatical quality of the sentences they produce. The usual NLP approach to this problem is based on parsing student-generated text. Here, we propose a generation-based ap- proach aiming at preventing errors ("scaffolding"). In our ICALL system, the student constructs sentences by composing syntactic trees out of lexically anchored "treelets" via a graphical drag & drop user interface. A natural-language generator computes all possible grammatically well-formed sentences entailed by the student-composed tree. It provides positive feedback if the student-composed tree belongs to the well-formed set, and negative feedback otherwise. If so requested by the student, it can substantiate the positive or negative feedback based on a comparison between the student-composed tree and its own trees (informative feedback on demand). In case of negative feedback, the system refuses to build the structure attempted by the student. Frequently occurring errors are handled in terms of "malrules." The system we describe is a prototype (implemented in JAVA and C++) which can be parameterized with respect to L1 and L2, the size of the lexicon, and the level of detail of the visually presented grammatical structures. -
Kempen, G., & Vosse, T. (1992). A language-sensitive text editor for Dutch. In P. O’Brian Holt, & N. Williams (
Eds. ), Computers and writing: State of the art (pp. 68-77). Dordrecht: Kluwer Academic Publishers.Abstract
Modern word processors begin to offer a range of facilities for spelling, grammar and style checking in English. For the Dutch language hardly anything is available as yet. Many commercial word processing packages do include a hyphenation routine and a lexicon-based spelling checker but the practical usefulness of these tools is limited due to certain properties of Dutch orthography, as we will explain below. In this chapter we describe a text editor which incorporates a great deal of lexical, morphological and syntactic knowledge of Dutch and monitors the orthographical quality of Dutch texts. Section 1 deals with those aspects of Dutch orthography which pose problems to human authors as well as to computational language sensitive text editing tools. In section 2 we describe the design and the implementation of the text editor we have built. Section 3 is mainly devoted to a provisional evaluation of the system. -
Kempen, G. (1992). Generation. In W. Bright (
Ed. ), International encyclopedia of linguistics (pp. 59-61). New York: Oxford University Press. -
Kempen, G. (1992). Language technology and language instruction: Computational diagnosis of word level errors. In M. Swartz, & M. Yazdani (
Eds. ), Intelligent tutoring systems for foreign language learning: The bridge to international communication (pp. 191-198). Berlin: Springer. -
Kempen, G. (1992). Grammar based text processing. Document Management: Nieuwsbrief voor Documentaire Informatiekunde, 1(2), 8-10.
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Kempen, G. (1992). Second language acquisition as a hybrid learning process. In F. Engel, D. Bouwhuis, T. Bösser, & G. d'Ydewalle (
Eds. ), Cognitive modelling and interactive environments in language learning (pp. 139-144). Berlin: Springer. -
Kempen, G. (1983). Het artificiële-intelligentieparadigma. Ervaringen met een nieuwe methodologie voor cognitief-psychologisch onderzoek. In J. Raaijmakers, P. Hudson, & A. Wertheim (
Eds. ), Metatheoretische aspekten van de psychonomie (pp. 85-98). Deventer: Van Loghum Slaterus. -
Kempen, G. (1983). Natural language facilities in information systems: Asset or liability? In J. Van Apeldoorn (
Ed. ), Man and information technology: Towards friendlier systems (pp. 81-86). Delft University Press. -
Kempen, G., & Huijbers, P. (1983). The lexicalization process in sentence production and naming: Indirect election of words. Cognition, 14(2), 185-209. doi:10.1016/0010-0277(83)90029-X.
Abstract
A series of experiments is reported in which subjects describe simple visual scenes by means of both sentential and non-sentential responses. The data support the following statements about the lexicalization (word finding) process. (1) Words used by speakers in overt naming or sentence production responses are selected by a sequence of two lexical retrieval processes, the first yielding abstract pre-phonological items (Ll -items), the second one adding their phonological shapes (L2-items). (2) The selection of several Ll-items for a multi-word utterance can take place simultaneously. (3) A monitoring process is watching the output of Ll-lexicalization to check if it is in keeping with prevailing constraints upon utterance format. (4) Retrieval of the L2-item which corresponds with a given LI-item waits until the Ld-item has been checked by the monitor, and all other Ll-items needed for the utterance under construction have become available. A coherent picture of the lexicalization process begins to emerge when these characteristics are brought together with other empirical results in the area of naming and sentence production, e.g., picture naming reaction times (Seymour, 1979), speech errors (Garrett, 1980), and word order preferences (Bock, 1982). -
Kempen, G. (1983). Wat betekent taalvaardigheid voor informatiesystemen? TNO project: Maandblad voor toegepaste wetenschappen, 11, 401-403.
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