@inproceedings{trivino-morales-bueno-2000-spanish,
    title = "A {S}panish {POS} Tagger with Variable Memory",
    author = "Trivi{\~n}o, Jos{\'e}  and
      Morales-Bueno, Rafael",
    editor = "Lavelli, Alberto  and
      Carroll, John  and
      Berwick, Robert C.  and
      Bunt, Harry C.  and
      Carpenter, Bob  and
      Carroll, John  and
      Church, Ken  and
      Johnson, Mark  and
      Joshi, Aravind  and
      Kaplan, Ronald  and
      Kay, Martin  and
      Lang, Bernard  and
      Lavie, Alon  and
      Nijholt, Anton  and
      Samuelsson, Christer  and
      Steedman, Mark  and
      Stock, Oliviero  and
      Tanaka, Hozumi  and
      Tomita, Masaru  and
      Uszkoreit, Hans  and
      Vijay-Shanker, K.  and
      Weir, David  and
      Wiren, Mats",
    booktitle = "Proceedings of the Sixth International Workshop on Parsing Technologies",
    month = feb # " 23-25",
    year = "2000",
    address = "Trento, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2000.iwpt-1.25/",
    pages = "254--265",
    abstract = "An implementation of a Spanish POS tagger is described in this paper. This implementation combines three basic approaches: a single word tagger based on decision trees, a POS tagger based on variable memory Markov models, and a feature structures set of tags. Using decision trees for single word tagging allows the tagger to work without a lexicon that lists only possible tags. Moreover, it decreases the error rate because there are no unknown words. The feature structure set of tags is advantageous when the available training corpus is small and the tag set large, which can be the case with morphologically rich languages like Spanish. Finally, variable memory Markov models training is more efficient than traditional full-order Markov models and achieves better accuracy. In this implementation, 98.58{\%} of tokens are correctly classified."
}