@inproceedings{ehren-etal-2018-mumpitz,
    title = "{M}umpitz at {PARSEME} Shared Task 2018: A Bidirectional {LSTM} for the Identification of Verbal Multiword Expressions",
    author = "Ehren, Rafael  and
      Lichte, Timm  and
      Samih, Younes",
    editor = "Savary, Agata  and
      Ramisch, Carlos  and
      Hwang, Jena D.  and
      Schneider, Nathan  and
      Andresen, Melanie  and
      Pradhan, Sameer  and
      Petruck, Miriam R. L.",
    booktitle = "Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions ({LAW}-{MWE}-{C}x{G}-2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/W18-4929/",
    pages = "261--267",
    abstract = "In this paper, we describe Mumpitz, the system we submitted to the PARSEME Shared task on automatic identification of verbal multiword expressions (VMWEs). Mumpitz consists of a Bidirectional Recurrent Neural Network (BRNN) with Long Short-Term Memory (LSTM) units and a heuristic that leverages the dependency information provided in the PARSEME corpus data to differentiate VMWEs in a sentence. We submitted results for seven languages in the closed track of the task and for one language in the open track. For the open track we used the same system, but with pretrained instead of randomly initialized word embeddings to improve the system performance."
}