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Madrid, Spain

Jorge-Botana G.,Spanish University for Distance Education (UNED) | Jorge-Botana G.,Semantia Laboratory | Olmos R.,Autonomous University of Madrid | Olmos R.,Semantia Laboratory | Barroso A.,Semantia Laboratory
Informatica (Slovenia) | Year: 2015

This study stems from a previous article [1] in which we found that a psycholinguistically motivated mechanism based on the Construction-Integration (C-I) model [2,3] could be used for call classifiers in systems based on Latent Semantic Analysis (LSA). In it we showed that with this model more robust results were obtained when categorizing call transcriptions. However, this method was not tested in a context of calls in audio format, where a voice recognition application would be involved. The most direct implication of a voice recognition application is that the text to be categorized may be impoverished and is subject to noise. This impoverishment normally translates into deletions and insertions which are semantically arbitrary but phonetically similar. The aim of this study is to describe the behavior of a complete system, with calls in audio format that are transcribed by a voice recognition application using a Stochastic Language Model (SLM), and then categorized with an LSA model. This process optionally includes a mechanism based on the C-I model. In this study different parameters were analyzed to assess the automatic router's rate of correct choices. The results show that once again the model based on C-I is significantly better, but the benefits are more remarkable when the utterances are long. The paper describes the system and examines both the full results and the interactions in some scenarios. The economy of resources and flexibility of the system are also discussed. Source


Olmos R.,Autonomous University of Madrid | Olmos R.,Semantia Laboratory | Jorge-Botana G.,Spanish University for Distance Education (UNED) | Jorge-Botana G.,Semantia Laboratory | And 4 more authors.
Information Processing and Management | Year: 2016

The purpose of this article is to validate, through two empirical studies, a new method for automatic evaluation of written texts, called Inbuilt Rubric, based on the Latent Semantic Analysis (LSA) technique, which constitutes an innovative and distinct turn with respect to LSA application so far. In the first empirical study, evidence of the validity of the method to identify and evaluate the conceptual axes of a text in a sample of 78 summaries by secondary school students is sought. Results show that the proposed method has a significantly higher degree of reliability than classic LSA methods of text evaluation, and displays very high sensitivity to identify which conceptual axes are included or not in each summary. A second study evaluates the method's capacity to interact and provide feedback about quality in a real online system on a sample of 924 discursive texts written by university students. Results show that students improved the quality of their written texts using this system, and also rated the experience very highly. The final conclusion is that this new method opens a very interesting way regarding the role of automatic assessors in the identification of presence/absence and quality of elaboration of relevant conceptual information in texts written by students with lower time costs than the usual LSA-based methods. © 2015 Elsevier Ltd. All rights reserved. Source

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