Summary of the paper

Title Unsupervised Lexical Acquisition for Part of Speech Tagging
Authors Dan Tufis, Elena Irimia, Radu Ion and Alexandru Ceausu
Abstract It is known that POS tagging is not very accurate for unknown words (words which the POS tagger has not seen in the training corpora). Thus, a first step to improve the tagging accuracy would be to extend the coverage of the tagger’s learned lexicon. It turns out that, through the use of a simple procedure, one can extend this lexicon without using additional, hard to obtain, hand-validated training corpora. The basic idea consists of merely adding new words along with their (correct) POS tags to the lexicon and trying to estimate the lexical distribution of these words according to similar ambiguity classes already present in the lexicon. We present a method of automatically acquire high quality POS tagging lexicons based on morphologic analysis and generation. Currently, this procedure works on Romanian for which we have a required paradigmatic generation procedure but the architecture remains general in the sense that given the appropriate substitutes for the morphological generator and POS tagger, one should obtain similar results.
Language Single language
Topics Acquisition, Machine Learning, Lexicon, lexical database, Tagging
Full paper Unsupervised Lexical Acquisition for Part of Speech Tagging
Slides -
Bibtex @InProceedings{TUFIS08.56,
  author = {Dan Tufis, Elena Irimia, Radu Ion and Alexandru Ceausu},
  title = {Unsupervised Lexical Acquisition for Part of Speech Tagging},
  booktitle = {Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)},
  year = {2008},
  month = {may},
  date = {28-30},
  address = {Marrakech, Morocco},
  editor = {Nicoletta Calzolari (Conference Chair), Khalid Choukri, Bente Maegaard, Joseph Mariani, Jan Odijk, Stelios Piperidis, Daniel Tapias},
  publisher = {European Language Resources Association (ELRA)},
  isbn = {2-9517408-4-0},
  note = {},
  language = {english}

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