Distributed Representations, Simple Recurrent Networks, and Grammatical Structure
ثبت نشده
چکیده
منابع مشابه
Neural Network Based Grammatical Learning and its Application for Structure Identification
Structure identification has been used widely in many contexts. Grammatical Learning methods are used to find structure information through sequences. Due to negative results, alternative representations have to be used for Grammatical Learning. One such representation is recurrent neural network. Recurrent neural networks are proposed as extended automata. In this chapter, we first summarize r...
متن کاملRecent Advances of Grammatical Inference
In this paper, we provide a survey of recent advances in the field “Grammatical Inference” with a particular emphasis on the results concerning the learnability of target classes represented by deterministic finite automata, context-free grammars, hidden Markov models, stochastic contextfree grammars, simple recurrent neural networks, and case-based representations.
متن کاملSolving Linear Semi-Infinite Programming Problems Using Recurrent Neural Networks
Linear semi-infinite programming problem is an important class of optimization problems which deals with infinite constraints. In this paper, to solve this problem, we combine a discretization method and a neural network method. By a simple discretization of the infinite constraints,we convert the linear semi-infinite programming problem into linear programming problem. Then, we use...
متن کاملTail-Recursive Distributed Representations and Simple Recurrent Networks
Representation poses important challenges to connectionism. The ability to structurally compose representations is critical in achieving the capability considered necessary for cognition. We are investigating distributed patterns that represent structure as part of a larger effort to develop a natural language processor. Recursive Auto-Associative Memory (RAAM) representations show unusual prom...
متن کاملThematic Representation in Simple Recurrent Networks
Introduction Simple recurrent networks (SRNs) are able to learn and represent lexical classes (Elman, 1990) and grammatical knowledge, such as agreement and argument structure (Elman, 1991), on the basis of co-occurrence regularities embedded in simple and complex sentences. In the present study, we address the question whether SRNs can represent differences in the thematic roles assigned by ve...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 1991