Probabilistic Refinement Algorithms for the Generation of Referring Expressions

Altamirano, R., Areces, C., and Benotti, L.. Probabilistic Refinement Algorithms for the Generation of Referring Expressions. In Proceedings of the 24th International Conference on Computational Linguistics (COLING 2012), pp. 53–62, Mumbai, India, December 2012.

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Abstract:

We propose an algorithm for the generation of referring expressions that adapts the approach of Areces et al. (2008, 2011) to include overspecification and probabilities learned from corpora. After introducing the algorithm, we discuss how probabilities required as input can be computed for any given domain for which a suitable corpus of REs is available, and how the probabilities can be adjusted for new scenes in the domain using a machine learning approach. We exemplify how to compute probabilities over the GRE3D7 corpus of Viethen (2011). The resulting algorithm is able to generate different referring expressions for the same target with a frequency similar to that observed in corpora. We empirically evaluate the new algorithm over the GRE3D7 corpus, and show that the probability distribution of the generated referring expressions match the one found in the corpus with high accuracy.

BibTeX: (download)

@INPROCEEDINGS{alta:prob12,
  author = {Altamirano, R. and Areces, C. and Benotti, L.},
  title = {Probabilistic Refinement Algorithms for the Generation of Referring
	Expressions},
  booktitle = {Proceedings of the 24th International Conference on Computational
	Linguistics (COLING 2012)},
  year = {2012},
  pages = {53--62},
  address = {Mumbai, India},
  month = {December},
  abstract = {We propose an algorithm for the generation of referring expressions
	that adapts the approach of Areces et al. (2008, 2011) to include
	overspecification and probabilities learned from corpora. After introducing
	the algorithm, we discuss how probabilities required as input can
	be computed for any given domain for which a suitable corpus of REs
	is available, and how the probabilities can be adjusted for new scenes
	in the domain using a machine learning approach. We exemplify how
	to compute probabilities over the GRE3D7 corpus of Viethen (2011).
	The resulting algorithm is able to generate different referring expressions
	for the same target with a frequency similar to that observed in
	corpora. We empirically evaluate the new algorithm over the GRE3D7
	corpus, and show that the probability distribution of the generated
	referring expressions match the one found in the corpus with high
	accuracy.},
  owner = {areces},
  timestamp = {2012.10.30},
  url = {http://www.aclweb.org/anthology/C12-2006}
}

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