5 research outputs found
Spectral learning of transducers over continuous sequences
In this paper we present a spectral algorithm for learning weighted nite state transducers (WFSTs) over paired input-output sequences, where the input is continuous and the output discrete. WFSTs are an important tool for modeling paired input-output sequences and have numerous applications in
real-world problems. Recently, Balle et al (2011) proposed a spectral method for learning WFSTs that overcomes some of the well known limitations of gradient-based or EM optimizations which can be computationally expensive and su er from local optima issues. Their algorithm can model distributions where both inputs and outputs are sequences from a discrete alphabet.
However, many real world problems require modeling paired sequences where the inputs are not discrete but continuos sequences. Modelling continuous sequences with spectral methods has been studied in the context of HMMs (Song et al 2010), where a spectral algorithm for this case was derived. In this
paper we follow that line of work and propose a spectral learning algorithm
for modelling paired input-output sequences where the inputs are continuous and the outputs are discrete. Our approach is based on generalizing the class of weighted nite state transducers over discrete input-output sequences to a class where transitions are linear combinations of elementary transitions and the weights of this linear combinations are determined by dynamic features of the continuous input sequence.
At its core, the algorithm is simple and scalable to large data sets. We present experiments on a real task that validate the eff ectiveness of the proposed approach.Postprint (published version
Constrained domain maximum likelihood estimation and the loss function in statistical pattern recognition
In this thesis we present a new estimation algorithm for statistical models which does not incurs in the over-trainning problems. This new estimation techinque, the so-called, constrained domain maximum likelihood estimation (CDMLE) holds all the theoretical properties of the maximum likelihood estimation and furthermore it does not provides overtrained parameter sets.
On the other hand, the impliations of the the 0-1 loss function assumption are analysed in the pattern recognition tasks. Specifically, more versatile functions are designed without increasing the optimal classification rule costs. This approach is applied to the statistical machine translation problem.Andrés Ferrer, J. (2008). Constrained domain maximum likelihood estimation and the loss function in statistical pattern recognition. http://hdl.handle.net/10251/13638Archivo delegad
Inference of Finite-State Transducers By Using Regular Grammars and Morphisms
A technique to infer finite-state transducers is proposed in this work. This technique is based on the formal relations between finite-state transducers and regular grammars. The technique consists of: 1) building a corpus of training strings from the corpus of training pairs; 2) inferring a regular grammar and 3) transforming the grammar into a finite-state transducer
Novel statistical approaches to text classification, machine translation and computer-assisted translation
Esta tesis presenta diversas contribuciones en los campos de la
clasificación automática de texto, traducción automática y traducción
asistida por ordenador bajo el marco estadÃstico.
En clasificación automática de texto, se propone una nueva aplicación
llamada clasificación de texto bilingüe junto con una serie de modelos
orientados a capturar dicha información bilingüe. Con tal fin se
presentan dos aproximaciones a esta aplicación; la primera de ellas se
basa en una asunción naive que contempla la independencia entre las
dos lenguas involucradas, mientras que la segunda, más sofisticada,
considera la existencia de una correlación entre palabras en
diferentes lenguas. La primera aproximación dió lugar al desarrollo de
cinco modelos basados en modelos de unigrama y modelos de n-gramas
suavizados. Estos modelos fueron evaluados en tres tareas de
complejidad creciente, siendo la más compleja de estas tareas
analizada desde el punto de vista de un sistema de ayuda a la
indexación de documentos. La segunda aproximación se caracteriza por
modelos de traducción capaces de capturar correlación entre palabras
en diferentes lenguas. En nuestro caso, el modelo de traducción
elegido fue el modelo M1 junto con un modelo de unigramas. Este
modelo fue evaluado en dos de las tareas más simples superando la
aproximación naive, que asume la independencia entre palabras en
differentes lenguas procedentes de textos bilingües.
En traducción automática, los modelos estadÃsticos de traducción
basados en palabras M1, M2 y HMM son extendidos bajo el marco de la
modelización mediante mixturas, con el objetivo de definir modelos de
traducción dependientes del contexto. Asimismo se extiende un
algoritmo iterativo de búsqueda basado en programación dinámica,
originalmente diseñado para el modelo M2, para el caso de mixturas de
modelos M2. Este algoritmo de búsqueda nCivera Saiz, J. (2008). Novel statistical approaches to text classification, machine translation and computer-assisted translation [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/2502Palanci