@InProceedings{dadashkarimi-jalilisabet-shakery:2016:COLING,
  author    = {Dadashkarimi, Javid  and  Jalili Sabet, Masoud  and  Shakery, Azadeh},
  title     = {Learning to Weight Translations using Ordinal Linear Regression and Query-generated Training Data for Ad-hoc Retrieval with Long Queries},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {1725--1733},
  abstract  = {Ordinal regression which is known with learning to rank has long been used in
	information retrieval (IR). Learning to rank algorithms, have been tailored in
	document ranking, information filtering, and building large aligned corpora
	successfully.
	In this paper, we propose to use this algorithm for query modeling in
	cross-language environments.
	To this end, first we build a query-generated training data using
	pseudo-relevant documents to the query and all translation candidates.
	The pseudo-relevant documents are obtained by top-ranked documents in response
	to a translation of the original query.
	The class of each candidate in the training data is determined based on
	presence/absence of the candidate in the pseudo-relevant documents.
	We learn an ordinal regression model to score the candidates based on their
	relevance to the context of the query, and after that, we construct a
	query-dependent translation model using a softmax function. Finally, we
	re-weight the query based on the obtained model. 
	Experimental results on French, German, Spanish, and Italian CLEF collections
	demonstrate that the proposed method achieves better results compared to
	state-of-the-art cross-language information retrieval methods, particularly in
	long queries with large training data.},
  url       = {http://aclweb.org/anthology/C16-1162}
}