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        <title>Training Language Models without Appropriate Language Resources: Experiments with an AAC System for Disabled People</title>
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    <name type="personal">
        <namePart type="given">Tonio</namePart>
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    <name type="personal">
        <namePart type="given">Jean-Yves</namePart>
        <namePart type="family">Antoine</namePart>
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            <title>Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)</title>
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        <name type="personal">
            <namePart type="given">Nicoletta</namePart>
            <namePart type="family">Calzolari</namePart>
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        <name type="personal">
            <namePart type="given">Khalid</namePart>
            <namePart type="family">Choukri</namePart>
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        <name type="personal">
            <namePart type="given">Aldo</namePart>
            <namePart type="family">Gangemi</namePart>
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            <namePart type="given">Bente</namePart>
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            <namePart type="given">Joseph</namePart>
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        <name type="personal">
            <namePart type="given">Jan</namePart>
            <namePart type="family">Odijk</namePart>
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        <name type="personal">
            <namePart type="given">Daniel</namePart>
            <namePart type="family">Tapias</namePart>
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    <abstract>Statistical Language Models (LM) are highly dependent on their training resources. This makes it not only difficult to interpret evaluation results, it also has a deteriorating effect on the use of an LM-based application. This question has already been studied by others. Considering a specific domain (text prediction in a communication aid for handicapped people) we want to address the problem from a different point of view: the influence of the language register. Considering corpora from five different registers, we want to discuss three methods to adapt a language model to its actual language resource ultimately reducing the effect of training dependency: (a) A simple cache model augmenting the probability of the n last inserted words; (b) a user dictionary, keeping every unseen word; and (c) a combined LM interpolating a base model with a dynamically updated user model. Our evaluation is based on the results obtained from a text prediction system working on a trigram LM.</abstract>
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        <url>https://aclanthology.org/L06-1059/</url>
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%0 Conference Proceedings
%T Training Language Models without Appropriate Language Resources: Experiments with an AAC System for Disabled People
%A Wandmacher, Tonio
%A Antoine, Jean-Yves
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Gangemi, Aldo
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Odijk, Jan
%Y Tapias, Daniel
%S Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)
%D 2006
%8 May
%I European Language Resources Association (ELRA)
%C Genoa, Italy
%F wandmacher-antoine-2006-training
%X Statistical Language Models (LM) are highly dependent on their training resources. This makes it not only difficult to interpret evaluation results, it also has a deteriorating effect on the use of an LM-based application. This question has already been studied by others. Considering a specific domain (text prediction in a communication aid for handicapped people) we want to address the problem from a different point of view: the influence of the language register. Considering corpora from five different registers, we want to discuss three methods to adapt a language model to its actual language resource ultimately reducing the effect of training dependency: (a) A simple cache model augmenting the probability of the n last inserted words; (b) a user dictionary, keeping every unseen word; and (c) a combined LM interpolating a base model with a dynamically updated user model. Our evaluation is based on the results obtained from a text prediction system working on a trigram LM.
%U https://aclanthology.org/L06-1059/

Markdown (Informal)

[Training Language Models without Appropriate Language Resources: Experiments with an AAC System for Disabled People](https://aclanthology.org/L06-1059/) (Wandmacher & Antoine, LREC 2006)

• Training Language Models without Appropriate Language Resources: Experiments with an AAC System for Disabled People (Wandmacher & Antoine, LREC 2006)

ACL

• Tonio Wandmacher and Jean-Yves Antoine. 2006. Training Language Models without Appropriate Language Resources: Experiments with an AAC System for Disabled People. In Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06), Genoa, Italy. European Language Resources Association (ELRA).