Alejandra Lorenzo


2013

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Unsupervised structured semantic inference for spoken dialog reservation tasks
Alejandra Lorenzo | Lina Rojas-Barahona | Christophe Cerisara
Proceedings of the SIGDIAL 2013 Conference

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Weakly and Strongly Constrained Dialogues for Language Learning
Claire Gardent | Alejandra Lorenzo | Laura Perez-Beltrachini | Lina Rojas-Barahona
Proceedings of the SIGDIAL 2013 Conference

2012

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An End-to-End Evaluation of Two Situated Dialog Systems
Lina M. Rojas-Barahona | Alejandra Lorenzo | Claire Gardent
Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue

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Unsupervised frame based Semantic Role Induction: application to French and English
Alejandra Lorenzo | Christophe Cerisara
Proceedings of the ACL 2012 Joint Workshop on Statistical Parsing and Semantic Processing of Morphologically Rich Languages

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Building and Exploiting a Corpus of Dialog Interactions between French Speaking Virtual and Human Agents
Lina M. Rojas-Barahona | Alejandra Lorenzo | Claire Gardent
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

We describe the acquisition of a dialog corpus for French based on multi-task human-machine interactions in a serious game setting. We present a tool for data collection that is configurable for multiple games; describe the data collected using this tool and the annotation schema used to annotate it; and report on the results obtained when training a classifier on the annotated data to associate each player turn with a dialog move usable by a rule based dialog manager. The collected data consists of approximately 1250 dialogs, 10454 utterances and 168509 words and will be made freely available to academic and nonprofit research.

2010

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Identifying Sources of Weakness in Syntactic Lexicon Extraction
Claire Gardent | Alejandra Lorenzo
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

Previous work has shown that large scale subcategorisation lexicons could be extracted from parsed corpora with reasonably high precision. In this paper, we apply a standard extraction procedure to a 100 millions words parsed corpus of french and obtain rather poor results. We investigate different factors likely to improve performance such as in particular, the specific extraction procedure and the parser used; the size of the input corpus; and the type of frames learned. We try out different ways of interleaving the output of several parsers with the lexicon extraction process and show that none of them improves the results. Conversely, we show that increasing the size of the input corpus and modifying the extraction procedure to better differentiate prepositional arguments from prepositional modifiers improves performance. In conclusion, we suggest that a more sophisticated approach to parser combination and better probabilistic models of the various types of prepositional objects in French are likely ways to get better results.