@inproceedings{ke-marteau-2014-co,
    title = "Co-clustering of bilingual datasets as a mean for assisting the construction of thematic bilingual comparable corpora",
    author = "Ke, Guiyao  and
      Marteau, Pierre-Francois",
    editor = "Calzolari, Nicoletta  and
      Choukri, Khalid  and
      Declerck, Thierry  and
      Loftsson, Hrafn  and
      Maegaard, Bente  and
      Mariani, Joseph  and
      Moreno, Asuncion  and
      Odijk, Jan  and
      Piperidis, Stelios",
    booktitle = "Proceedings of the Ninth International Conference on Language Resources and Evaluation ({LREC}`14)",
    month = may,
    year = "2014",
    address = "Reykjavik, Iceland",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/L14-1677/",
    pages = "1992--1999",
    abstract = "We address in this paper the assisted construction of bilingual thematic comparable corpora by means of co-clustering bilingual documents collected from raw sources such as the Web. The proposed approach is based on a quantitative comparability measure and a co-clustering approach which allow to mix similarity measures existing in each of the two linguistic spaces with a {\textquotedblleft}thematic{\textquotedblright} comparability measure that defines a mapping between these two spaces. With the improvement of the co-clustering ($k$-medoids) performance we get, we use a comparability threshold and a manual verification to ensure the good and robust alignment of co-clusters (co-medoids). Finally, from any available raw corpus, we enrich the aligned clusters in order to provide {\textquotedblleft}thematic{\textquotedblright} comparable corpora of good quality and controlled size. On a case study that exploit raw web data, we show that this approach scales reasonably well and is quite suited for the construction of thematic comparable corpora of good quality."
}