2,406 research outputs found
The Unsupervised Acquisition of a Lexicon from Continuous Speech
We present an unsupervised learning algorithm that acquires a
natural-language lexicon from raw speech. The algorithm is based on the optimal
encoding of symbol sequences in an MDL framework, and uses a hierarchical
representation of language that overcomes many of the problems that have
stymied previous grammar-induction procedures. The forward mapping from symbol
sequences to the speech stream is modeled using features based on articulatory
gestures. We present results on the acquisition of lexicons and language models
from raw speech, text, and phonetic transcripts, and demonstrate that our
algorithm compares very favorably to other reported results with respect to
segmentation performance and statistical efficiency.Comment: 27 page technical repor
Discovery of Linguistic Relations Using Lexical Attraction
This work has been motivated by two long term goals: to understand how humans
learn language and to build programs that can understand language. Using a
representation that makes the relevant features explicit is a prerequisite for
successful learning and understanding. Therefore, I chose to represent
relations between individual words explicitly in my model. Lexical attraction
is defined as the likelihood of such relations. I introduce a new class of
probabilistic language models named lexical attraction models which can
represent long distance relations between words and I formalize this new class
of models using information theory.
Within the framework of lexical attraction, I developed an unsupervised
language acquisition program that learns to identify linguistic relations in a
given sentence. The only explicitly represented linguistic knowledge in the
program is lexical attraction. There is no initial grammar or lexicon built in
and the only input is raw text. Learning and processing are interdigitated. The
processor uses the regularities detected by the learner to impose structure on
the input. This structure enables the learner to detect higher level
regularities. Using this bootstrapping procedure, the program was trained on
100 million words of Associated Press material and was able to achieve 60%
precision and 50% recall in finding relations between content-words. Using
knowledge of lexical attraction, the program can identify the correct relations
in syntactically ambiguous sentences such as ``I saw the Statue of Liberty
flying over New York.''Comment: dissertation, 56 page
Learning probability distributions generated by finite-state machines
We review methods for inference of probability distributions generated by probabilistic automata and related models for sequence generation. We focus on methods that can be proved to learn in the inference
in the limit and PAC formal models. The methods we review are state merging and state splitting methods for probabilistic deterministic automata and the recently developed spectral method for nondeterministic probabilistic automata. In both cases, we derive them from a high-level algorithm described in terms of the Hankel matrix of the distribution to be learned, given as an oracle, and then describe how to adapt that algorithm to account for the error introduced by a finite sample.Peer ReviewedPostprint (author's final draft
Hybrid modeling, HMM/NN architectures, and protein applications
We describe a hybrid modeling approach where the parameters of a model are calculated and modulated by another model, typically a neural network (NN), to avoid both overfitting and underfitting. We develop the approach for the case of Hidden Markov Models (HMMs), by deriving a class of hybrid HMM/NN architectures. These architectures can be trained with unified algorithms that blend HMM dynamic programming with NN backpropagation. In the case of complex data, mixtures of HMMs or modulated HMMs must be used. NNs can then be applied both to the parameters of each single HMM, and to the switching or modulation of the models, as a function of input or context. Hybrid HMM/NN architectures provide a flexible NN parameterization for the control of model structure and complexity. At the same time, they can capture distributions that, in practice, are inaccessible to single HMMs. The HMM/NN hybrid approach is tested, in its simplest form, by constructing a model of the immunoglobulin protein family. A hybrid model is trained, and a multiple alignment derived, with less than a fourth of the number of parameters used with previous single HMMs
- …