Connectionist Approaches to Language Learning

✍ Scribed by David S. Touretzky (auth.), David Touretzky (eds.)

• Publisher: Springer US
• Year: 1991
• Tongue: English
• Leaves: 150
• Series: The Springer International Series in Engineering and Computer Science 154
• Edition: 1
• Category: Library

✦ Synopsis

arise automatically as a result of the recursive structure of the task and the continuous nature of the SRN's state space. Elman also introduces a new graphical technique for study­ ing network behavior based on principal components analysis. He shows that sentences with multiple levels of embedding produce state space trajectories with an intriguing self­ similar structure. The development and shape of a recurrent network's state space is the subject of Pollack's paper, the most provocative in this collection. Pollack looks more closely at a connectionist network as a continuous dynamical system. He describes a new type of machine learning phenomenon: induction by phase transition. He then shows that under certain conditions, the state space created by these machines can have a fractal or chaotic structure, with a potentially infinite number of states. This is graphically illustrated using a higher-order recurrent network trained to recognize various regular languages over binary strings. Finally, Pollack suggests that it might be possible to exploit the fractal dynamics of these systems to achieve a generative capacity beyond that of finite-state machines.

✦ Table of Contents

Front Matter....Pages i-iv
Introduction....Pages 1-3
Learning Automata from Ordered Examples....Pages 5-34
SLUG: A Connectionist Architecture for Inferring the Structure of Finite-State Environments....Pages 35-56
Graded State Machines: The Representation of Temporal Contingencies in Simple Recurrent Networks....Pages 57-89
Distributed Representations, Simple Recurrent Networks, and Grammatical Structure....Pages 91-121
The Induction of Dynamical Recognizers....Pages 123-148
Back Matter....Pages 149-149

✦ Subjects

Artificial Intelligence (incl. Robotics);Statistical Physics, Dynamical Systems and Complexity;Computer Science, general