247 research outputs found
DESQ: Frequent Sequence Mining with Subsequence Constraints
Frequent sequence mining methods often make use of constraints to control
which subsequences should be mined. A variety of such subsequence constraints
has been studied in the literature, including length, gap, span,
regular-expression, and hierarchy constraints. In this paper, we show that many
subsequence constraints---including and beyond those considered in the
literature---can be unified in a single framework. A unified treatment allows
researchers to study jointly many types of subsequence constraints (instead of
each one individually) and helps to improve usability of pattern mining systems
for practitioners. In more detail, we propose a set of simple and intuitive
"pattern expressions" to describe subsequence constraints and explore
algorithms for efficiently mining frequent subsequences under such general
constraints. Our algorithms translate pattern expressions to compressed finite
state transducers, which we use as computational model, and simulate these
transducers in a way suitable for frequent sequence mining. Our experimental
study on real-world datasets indicates that our algorithms---although more
general---are competitive to existing state-of-the-art algorithms.Comment: Long version of the paper accepted at the IEEE ICDM 2016 conferenc
HFST—Framework for Compiling and Applying Morphologies
HFST–Helsinki Finite-State Technology ( hfst.sf.net ) is a framework for compiling and applying linguistic descriptions with finite-state methods. HFST currently connects some of the most important finite-state tools for creating morphologies and spellers into one open-source platform and supports extending and improving the descriptions with weights to accommodate the modeling of statistical information. HFST offers a path from language descriptions to efficient language applications in key environments and operating systems. HFST also provides an opportunity to exchange transducers between different software providers in order to get the best out of each finite-state library.Peer reviewe
Sampling from Stochastic Finite Automata with Applications to CTC Decoding
Stochastic finite automata arise naturally in many language and speech
processing tasks. They include stochastic acceptors, which represent certain
probability distributions over random strings. We consider the problem of
efficient sampling: drawing random string variates from the probability
distribution represented by stochastic automata and transformations of those.
We show that path-sampling is effective and can be efficient if the
epsilon-graph of a finite automaton is acyclic. We provide an algorithm that
ensures this by conflating epsilon-cycles within strongly connected components.
Sampling is also effective in the presence of non-injective transformations of
strings. We illustrate this in the context of decoding for Connectionist
Temporal Classification (CTC), where the predictive probabilities yield
auxiliary sequences which are transformed into shorter labeling strings. We can
sample efficiently from the transformed labeling distribution and use this in
two different strategies for finding the most probable CTC labeling
Fast and Accurate Keyword Spotting System
Tato práce se zabývá rychlou a přesnou detekcí klíčových slov z audio nahrávek. Cílem práce bylo prostudovat možnosti detekce slov a vytvořit několik typů jazykových modelů. Tyto modely následně mezi sebou porovnat. Zaměřujeme se zde na detekci klíčových slov z anglicky namluvených audio nahrávek.This bachelor's thesis deals with fast and accurate detection of keywords from audio records. The aim of was to study possibilities of word detection and to create several types of language models. These were then to be compared to each other. We focus here on the detection of keywords from English spoken audio records.
On the Disambiguation of Weighted Automata
We present a disambiguation algorithm for weighted automata. The algorithm
admits two main stages: a pre-disambiguation stage followed by a transition
removal stage. We give a detailed description of the algorithm and the proof of
its correctness. The algorithm is not applicable to all weighted automata but
we prove sufficient conditions for its applicability in the case of the
tropical semiring by introducing the *weak twins property*. In particular, the
algorithm can be used with all acyclic weighted automata, relevant to
applications. While disambiguation can sometimes be achieved using
determinization, our disambiguation algorithm in some cases can return a result
that is exponentially smaller than any equivalent deterministic automaton. We
also present some empirical evidence of the space benefits of disambiguation
over determinization in speech recognition and machine translation
applications
Automatic Diachronic Normalization of Polish Texts
The paper presents a method for the automatic diachronic normalization of Polish texts – the procedure, which, for a given historical text, returns its contemporary spelling. The method applies finite-state transducers, defined in a sublanguage of the Thrax formalism. The paper discusses linguistic issues, such as evolution in spelling of the Polish language, as well as implementation aspects, such as efficiency or testing the proposed method.The paper presents a method for the automatic diachronic normalization of Polish texts – the procedure, which, for a given historical text, returns its contemporary spelling. The method applies finite-state transducers, defined in a sublanguage of the Thrax formalism. The paper discusses linguistic issues, such as evolution in spelling of the Polish language, as well as implementation aspects, such as efficiency or testing the proposed method
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