@inproceedings{repar-etal-2022-fusion,
    title = "Fusion of linguistic, neural and sentence-transformer features for improved term alignment",
    author = "Repar, Andraz  and
      Pollak, Senja  and
      Ul{\v{c}}ar, Matej  and
      Koloski, Boshko",
    editor = "Rapp, Reinhard  and
      Zweigenbaum, Pierre  and
      Sharoff, Serge",
    booktitle = "Proceedings of the BUCC Workshop within LREC 2022",
    month = jun,
    year = "2022",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2022.bucc-1.9/",
    pages = "61--66",
    abstract = "Crosslingual terminology alignment task has many practical applications. In this work, we propose an aligning method for the shared task of the 15th Workshop on Building and Using Comparable Corpora. Our method combines several different approaches into one cohesive machine learning model, based on SVM. From shared-task specific and external sources, we crafted four types of features: cognate-based, dictionary-based, embedding-based, and combined features, which combine aspects of the other three types. We added a post-processing re-scoring method, which reducess the effect of hubness, where some terms are nearest neighbours of many other terms. We achieved the average precision score of 0.833 on the English-French training set of the shared task."
}