Summary of the paper

Title Unsupervised Acquisition of Verb Subcategorization Frames from Shallow-Parsed Corpora
Authors Alessandro Lenci, Barbara McGillivray, Simonetta Montemagni and Vito Pirrelli
Abstract In this paper, we reported experiments of unsupervised automatic acquisition of Italian and English verb subcategorization frames (SCFs) from general and domain corpora. The proposed technique operates on syntactically shallow-parsed corpora on the basis of a limited number of search heuristics not relying on any previous lexico-syntactic knowledge about SCFs. Although preliminary, reported results are in line with state-of-the-art lexical acquisition systems. The issue of whether verbs sharing similar SCFs distributions happen to share similar semantic properties as well was also explored by clustering verbs that share frames with the same distribution using the Minimum Description Length Principle (MDL). First experiments in this direction were carried out on Italian verbs with encouraging results.
Language Language-independent
Topics Acquisition, Machine Learning, Corpus (creation, annotation, etc.), Lexicon, lexical database
Full paper Unsupervised Acquisition of Verb Subcategorization Frames from Shallow-Parsed Corpora
Slides Unsupervised Acquisition of Verb Subcategorization Frames from Shallow-Parsed Corpora
Bibtex @InProceedings{LENCI08.763,
  author = {Alessandro Lenci, Barbara McGillivray, Simonetta Montemagni and Vito Pirrelli},
  title = {Unsupervised Acquisition of Verb Subcategorization Frames from Shallow-Parsed Corpora},
  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 = {http://www.lrec-conf.org/proceedings/lrec2008/},
  language = {english}
  }

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