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Título:
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Unsupervised spectral learning of FSTs
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Autor/a:
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Bailly, Raphaël; Carreras Pérez, Xavier; Quattoni, Ariadna Julieta
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Otros autores:
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Universitat Politècnica de Catalunya. GPLN - Grup de Processament del Llenguatge Natural; Universitat Politècnica de Catalunya. LARCA - Laboratori d'Algorísmia Relacional, Complexitat i Aprenentatge |
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Abstract:
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Finite-State Transducers (FST) are a standard tool for modeling paired input output sequences and are used in numerous applications, ranging from computational biology to natural language processing. Recently Balle et al. [4] presented a spectral algorithm for learning FST from samples of aligned input-output sequences. In this paper we address the more realistic, yet challenging setting where the alignments are unknown to the learning algorithm. We frame FST learning as finding a low rank Hankel matrix satisfying constraints derived from observable statistics. Under this formulation, we provide identifiability results for FST distributions. Then, following previous work on rank minimization, we propose a regularized convex relaxation of this objective which is based on minimizing a nuclear norm penalty subject to linear constraints and can be solved efficiently. |
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Materia(s):
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-Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Llenguatge natural
-Learning algorithms
-Bioinformatics
-Matrix algebra
-Natural language processing systems
-Relaxation processes
-Computational biology
-Finite state transducers
-Linear constraints
-Low-rank Hankel matrixes
-Natural language processing
-Rank minimizations
-Spectral algorithm
-Spectral learning
-Algorismes d'aprenentage |
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Derechos:
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Tipo de documento:
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Artículo - Versión publicada
Objeto de conferencia |
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