Guillermo A. Cecchi

Also published as: Guillermo Cecchi


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Nearly-Unsupervised Hashcode Representations for Biomedical Relation Extraction
Sahil Garg | Aram Galstyan | Greg Ver Steeg | Guillermo Cecchi
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Recently, kernelized locality sensitive hashcodes have been successfully employed as representations of natural language text, especially showing high relevance to biomedical relation extraction tasks. In this paper, we propose to optimize the hashcode representations in a nearly unsupervised manner, in which we only use data points, but not their class labels, for learning. The optimized hashcode representations are then fed to a supervised classifi er following the prior work. This nearly unsupervised approach allows fine-grained optimization of each hash function, which is particularly suitable for building hashcode representations generalizing from a training set to a test set. We empirically evaluate the proposed approach for biomedical relation extraction tasks, obtaining significant accuracy improvements w.r.t. state-of-the-art supervised and semi-supervised approaches.


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Using Automated Metaphor Identification to Aid in Detection and Prediction of First-Episode Schizophrenia
E. Darío Gutiérrez | Guillermo Cecchi | Cheryl Corcoran | Philip Corlett
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

The diagnosis of serious mental health conditions such as schizophrenia is based on the judgment of clinicians whose training takes several years, and cannot be easily formalized into objective measures. However, previous research suggests there are disturbances in aspects of the language use of patients with schizophrenia. Using metaphor-identification and sentiment-analysis algorithms to automatically generate features, we create a classifier, that, with high accuracy, can predict which patients will develop (or currently suffer from) schizophrenia. To our knowledge, this study is the first to demonstrate the utility of automated metaphor identification algorithms for detection or prediction of disease.


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The emergence of the modern concept of introspection: a quantitative linguistic analysis
Iván Raskovsky | Diego Fernández Slezak | Carlos Diuk | Guillermo A. Cecchi
Proceedings of the NAACL HLT 2010 Young Investigators Workshop on Computational Approaches to Languages of the Americas