The Influence of Context on the Learning of Metrical Stress Systems Using Finite-State Machines

Languages vary in the way stress is assigned to syllables within words. This article investigates the learnability of stress systems in a wide range of languages. The stress systems can be described using finite-state automata with symbols indicating levels of stress (primary, secondary, or no stress). Finite-state automata have been the focus of research in the area of grammatical inference for some time now. It has been shown that finite-state machines are learnable from examples using state-merging. One such approach, which aims to learn k-testable languages, has been applied to stress systems with some success. The family of k-testable languages has been shown to be efficiently learnable (in polynomial time). Here, we extend this approach to k, l-local languages by taking not only left context, but also right context, into account. We consider empirical results testing the performance of our learner using various amounts of context (corresponding to varying definitions of phonological locality). Our results show that our approach of learning stress patterns using state-merging is more reliant on left context than on right context. Additionally, some stress systems fail to be learned by our learner using either the left-context k-testable or the left-and-right-context k, l-local learning system. A more complex merging strategy, and hence grammar representation, is required for these stress systems.

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An Algebraic Framework to Represent Finite State Machines in Single-Layer Recurrent Neural Networks

Neural Computation ◽

10.1162/neco.1995.7.5.931 ◽

1995 ◽

Vol 7 (5) ◽

pp. 931-949 ◽

Cited By ~ 21

Author(s):

R. Alquézar ◽

A. Sanfeliu

Keyword(s):

Neural Networks ◽

Recurrent Neural Networks ◽

Single Layer ◽

Finite State Machines ◽

Grammatical Inference ◽

State Machines ◽

Linear System Of Equations ◽

Wide Range ◽

Finite State ◽

Algebraic Framework

In this paper we present an algebraic framework to represent finite state machines (FSMs) in single-layer recurrent neural networks (SLRNNs), which unifies and generalizes some of the previous proposals. This framework is based on the formulation of both the state transition function and the output function of an FSM as a linear system of equations, and it permits an analytical explanation of the representational capabilities of first-order and higher-order SLRNNs. The framework can be used to insert symbolic knowledge in RNNs prior to learning from examples and to keep this knowledge while training the network. This approach is valid for a wide range of activation functions, whenever some stability conditions are met. The framework has already been used in practice in a hybrid method for grammatical inference reported elsewhere (Sanfeliu and Alquézar 1994).

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Pioneer: A New Tool for Coding of Multi-Level Finite State Machines Based on Evolution Programming

VLSI Design ◽

10.1155/1994/13748 ◽

1994 ◽

Vol 2 (2) ◽

pp. 105-116

Author(s):

S. Muddappa ◽

R. Z. Makki ◽

Z. Michalewicz ◽

S. Isukapalli

Keyword(s):

Finite State Machines ◽

Mutation Rates ◽

Natural Phenomenon ◽

State Machines ◽

State Assignment ◽

Wide Range ◽

Finite State ◽

Multi Level ◽

Crossover And Mutation ◽

Evolution Programs

In this paper we present a new tool for the encoding of multi-level finite state machines based on the concept of evolution programming. Evolution programs are stochastic adaptive algorithms, based on the paradigm of genetic algorithms whose search methods model some natural phenomenon: genetic inheritance and Darwinian strife for survival. Crossover and mutation rates were tailored to the state assignment problem experimentally. We present results over a wide range of MCNC benchmarks which demonstrate the effectiveness of the new tool. The results show that evolution programs can be effectively applied to state assignment.

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Finite-State Technology

The Oxford Handbook of Computational Linguistics 2nd edition ◽

10.1093/oxfordhb/9780199573691.013.39 ◽

2018 ◽

Author(s):

Mans Hulden

Keyword(s):

Natural Language Processing ◽

Natural Language ◽

Computational Linguistics ◽

Language Processing ◽

Finite State Machines ◽

Regular Languages ◽

Finite State Automata ◽

State Machines ◽

Computational Phonology ◽

Finite State

Finite-state machines—automata and transducers—are ubiquitous in natural-language processing and computational linguistics. This chapter introduces the fundamentals of finite-state automata and transducers, both probabilistic and non-probabilistic, illustrating the technology with example applications and common usage. It also covers the construction of transducers, which correspond to regular relations, and automata, which correspond to regular languages. The technologies introduced are widely employed in natural language processing, computational phonology and morphology in particular, and this is illustrated through common practical use cases.

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Efficient parallel derivation of short distinguishing sequences for nondeterministic finite state machines using MapReduce

Journal Of Big Data ◽

10.1186/s40537-021-00535-6 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Bilal Elghadyry ◽

Faissal Ouardi ◽

Zineb Lotfi ◽

Sébastien Verel

Keyword(s):

Finite State Machine ◽

Exact Algorithm ◽

Finite State Machines ◽

Identification Problem ◽

State Machine ◽

State Machines ◽

State Identification ◽

Distinguishing Sequences ◽

Wide Range ◽

Finite State

AbstractDistinguishing sequences are widely used in finite state machine-based conformance testing to solve the state identification problem. In this paper, we address the scalability issue encountered while deriving distinguishing sequences from complete observable nondeterministic finite state machines by introducing a massively parallel MapReduce version of the well-known Exact Algorithm. To the best of our knowledge, this is the first study to tackle this task using the MapReduce approach. First, we give a concise overview of the well-known Exact Algorithm for deriving distinguishing sequences from nondeterministic finite state machines. Second, we propose a parallel algorithm for this problem using the MapReduce approach and analyze its communication cost using Afrati et al. model. Furthermore, we conduct a variety of intensive and comparative experiments on a wide range of finite state machine classes to demonstrate that our proposed solution is efficient and scalable.

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STATE ENCODING OF FINITE STATE MACHINES FOR LOW POWER DESIGN

Journal of Circuits System and Computers ◽

10.1142/s0218126696000455 ◽

1996 ◽

Vol 06 (06) ◽

pp. 649-661 ◽

Cited By ~ 2

Author(s):

DE-SHENG CHEN ◽

MAJID SARRAFZADEH ◽

GARY K.H. YEAP

Keyword(s):

Low Power ◽

Power Dissipation ◽

Finite State Machines ◽

Low Power Design ◽

Design Flow ◽

State Machines ◽

Switching Activity ◽

Wide Range ◽

Finite State ◽

State Encoding

We address the problem of state encoding for synchronous finite state machines (FSMs), targeted for low power design. Most previous work in FSM state encoding has been focused on minimizing chip area and does not consider switching activity of the circuit. As a result, this does not always lead to a power efficient implementation. Especially in CMOS circuits, the switching activity is a very important factor to power dissipation. In this work, we define a function λ for automatic tradeoff between switching activity and area that contribute to power dissipation. λ is used in determining the encoding affinity between states and is observed to be related to the number of states of an FSM in our experiments. A state encoding algorithm, based on hypercube embedding, is proposed to find encodings of states such that the sum of bit toggles between each pair of states times the encoding affinity between them is minimized. The proposed approach does not require any change in the functional specification of the state machine and can be easily incorporated in present design flow. Results over a wide range of MCNC benchmark examples which show the efficacy of our technique are presented. A simple function for λ is provided, and it is shown to be robust in finding low-power state encodings.

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