Interactive Pattern Recognition applied to Natural Language Processing

Abstract

This thesis is about Pattern Recognition. In the last decades, huge efforts have been made to develop automatic systems able to rival human capabilities in this field. Although these systems achieve high productivity rates, they are not precise enough in most situations. Humans, on the contrary, are very accurate but comparatively quite slower. This poses an interesting question: the possibility of benefiting from both worlds by constructing cooperative systems. This thesis presents diverse contributions to this kind of collaborative approach. The point is to improve the Pattern Recognition systems by properly introducing a human operator into the system. We call this Interactive Pattern Recognition (IPR). Firstly, a general proposal for IPR will be stated. The aim is to develop a framework to easily derive new applications in this area. Some interesting IPR issues are also introduced. Multi-modality or adaptive learning are examples of extensions that can naturally fit into IPR. In the second place, we will focus on a specific application. A novel method to obtain high quality speech transcriptions (CAST, Computer Assisted Speech Transcription). We will start by proposing a CAST formalization and, next, we will cope with different implementation alternatives. Practical issues, as the system response time, will be also taken into account, in order to allow for a practical implementation of CAST. Word graphs and probabilistic error correcting parsing are tools that will be used to reach an alternative formulation that allows for the use of CAST in a real scenario. Afterwards, a special application within the general IPR framework will be discussed. This is intended to test the IPR capabilities in an extreme environment, where no input pattern is available and the system only has access to the user actions to produce a hypothesis. Specifically, we will focus here on providing assistance in the problem of text generation.