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        <title>The ATILF-LLF System for Parseme Shared Task: a Transition-based Verbal Multiword Expression Tagger</title>
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        <namePart type="given">Hazem</namePart>
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            <title>Proceedings of the 13th Workshop on Multiword Expressions (MWE 2017)</title>
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            <namePart type="given">Stella</namePart>
            <namePart type="family">Markantonatou</namePart>
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        <name type="personal">
            <namePart type="given">Carlos</namePart>
            <namePart type="family">Ramisch</namePart>
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            <namePart type="given">Veronika</namePart>
            <namePart type="family">Vincze</namePart>
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    <abstract>We describe the ATILF-LLF system built for the MWE 2017 Shared Task on automatic identification of verbal multiword expressions. We participated in the closed track only, for all the 18 available languages. Our system is a robust greedy transition-based system, in which MWE are identified through a MERGE transition. The system was meant to accommodate the variety of linguistic resources provided for each language, in terms of accompanying morphological and syntactic information. Using per-MWE Fscore, the system was ranked first for all but two languages (Hungarian and Romanian).</abstract>
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%0 Conference Proceedings
%T The ATILF-LLF System for Parseme Shared Task: a Transition-based Verbal Multiword Expression Tagger
%A Al Saied, Hazem
%A Constant, Matthieu
%A Candito, Marie
%Y Markantonatou, Stella
%Y Ramisch, Carlos
%Y Savary, Agata
%Y Vincze, Veronika
%S Proceedings of the 13th Workshop on Multiword Expressions (MWE 2017)
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F al-saied-etal-2017-atilf
%X We describe the ATILF-LLF system built for the MWE 2017 Shared Task on automatic identification of verbal multiword expressions. We participated in the closed track only, for all the 18 available languages. Our system is a robust greedy transition-based system, in which MWE are identified through a MERGE transition. The system was meant to accommodate the variety of linguistic resources provided for each language, in terms of accompanying morphological and syntactic information. Using per-MWE Fscore, the system was ranked first for all but two languages (Hungarian and Romanian).
%R 10.18653/v1/W17-1717
%U https://aclanthology.org/W17-1717
%U https://doi.org/10.18653/v1/W17-1717
%P 127-132

Markdown (Informal)

[The ATILF-LLF System for Parseme Shared Task: a Transition-based Verbal Multiword Expression Tagger](https://aclanthology.org/W17-1717) (Al Saied et al., MWE 2017)

• The ATILF-LLF System for Parseme Shared Task: a Transition-based Verbal Multiword Expression Tagger (Al Saied et al., MWE 2017)

ACL

• Hazem Al Saied, Matthieu Constant, and Marie Candito. 2017. The ATILF-LLF System for Parseme Shared Task: a Transition-based Verbal Multiword Expression Tagger. In Proceedings of the 13th Workshop on Multiword Expressions (MWE 2017), pages 127–132, Valencia, Spain. Association for Computational Linguistics.