SARDSRN: A NEURAL NETWORK SHIFT-REDUCE PARSER

Simple Recurrent Networks (SRNs) have been widely used in natural language tasks. SARDSRN extends the SRN by explicitly representing the input sequence in a SARDNET self-organizing map. The distributed SRN component leads to good generalization and robust cognitive properties, whereas the SARDNET map provides exact representations of the sentence constituents. This combination allows SARDSRN to learn to parse sentences with more complicated structure than can the SRN alone, and suggests that the approach could scale up to realistic natural language

Connectionist natural language parsing

The key developments of two decades of connectionist parsing are reviewed. Connectionist parsers are assessed according to their ability to learn to represent syntactic structures from examples automatically, without being presented with symbolic grammar rules. This review also considers the extent to which connectionist parsers offer computational models of human sentence processing and provide plausible accounts of psycholinguistic data. In considering these issues, special attention is paid to the level of realism, the nature of the modularity, and the type of processing that is to be found in a wide range of parsers

SKOPE: A connectionist/symbolic architecture of spoken Korean processing

Spoken language processing requires speech and natural language integration. Moreover, spoken Korean calls for unique processing methodology due to its linguistic characteristics. This paper presents SKOPE, a connectionist/symbolic spoken Korean processing engine, which emphasizes that: 1) connectionist and symbolic techniques must be selectively applied according to their relative strength and weakness, and 2) the linguistic characteristics of Korean must be fully considered for phoneme recognition, speech and language integration, and morphological/syntactic processing. The design and implementation of SKOPE demonstrates how connectionist/symbolic hybrid architectures can be constructed for spoken agglutinative language processing. Also SKOPE presents many novel ideas for speech and language processing. The phoneme recognition, morphological analysis, and syntactic analysis experiments show that SKOPE is a viable approach for the spoken Korean processing.Comment: 8 pages, latex, use aaai.sty & aaai.bst, bibfile: nlpsp.bib, to be presented at IJCAI95 workshops on new approaches to learning for natural language processin

Chart-driven Connectionist Categorial Parsing of Spoken Korean

While most of the speech and natural language systems which were developed for English and other Indo-European languages neglect the morphological processing and integrate speech and natural language at the word level, for the agglutinative languages such as Korean and Japanese, the morphological processing plays a major role in the language processing since these languages have very complex morphological phenomena and relatively simple syntactic functionality. Obviously degenerated morphological processing limits the usable vocabulary size for the system and word-level dictionary results in exponential explosion in the number of dictionary entries. For the agglutinative languages, we need sub-word level integration which leaves rooms for general morphological processing. In this paper, we developed a phoneme-level integration model of speech and linguistic processings through general morphological analysis for agglutinative languages and a efficient parsing scheme for that integration. Korean is modeled lexically based on the categorial grammar formalism with unordered argument and suppressed category extensions, and chart-driven connectionist parsing method is introduced.Comment: 6 pages, Postscript file, Proceedings of ICCPOL'9

Learning Fault-tolerant Speech Parsing with SCREEN

This paper describes a new approach and a system SCREEN for fault-tolerant speech parsing. SCREEEN stands for Symbolic Connectionist Robust EnterprisE for Natural language. Speech parsing describes the syntactic and semantic analysis of spontaneous spoken language. The general approach is based on incremental immediate flat analysis, learning of syntactic and semantic speech parsing, parallel integration of current hypotheses, and the consideration of various forms of speech related errors. The goal for this approach is to explore the parallel interactions between various knowledge sources for learning incremental fault-tolerant speech parsing. This approach is examined in a system SCREEN using various hybrid connectionist techniques. Hybrid connectionist techniques are examined because of their promising properties of inherent fault tolerance, learning, gradedness and parallel constraint integration. The input for SCREEN is hypotheses about recognized words of a spoken utterance potentially analyzed by a speech system, the output is hypotheses about the flat syntactic and semantic analysis of the utterance. In this paper we focus on the general approach, the overall architecture, and examples for learning flat syntactic speech parsing. Different from most other speech language architectures SCREEN emphasizes an interactive rather than an autonomous position, learning rather than encoding, flat analysis rather than in-depth analysis, and fault-tolerant processing of phonetic, syntactic and semantic knowledge.Comment: 6 pages, postscript, compressed, uuencoded to appear in Proceedings of AAAI 9

Having Your Cake and Eating It Too: Autonomy and Interaction in a Model of Sentence Processing

Is the human language understander a collection of modular processes operating with relative autonomy, or is it a single integrated process? This ongoing debate has polarized the language processing community, with two fundamentally different types of model posited, and with each camp concluding that the other is wrong. One camp puts forth a model with separate processors and distinct knowledge sources to explain one body of data, and the other proposes a model with a single processor and a homogeneous, monolithic knowledge source to explain the other body of data. In this paper we argue that a hybrid approach which combines a unified processor with separate knowledge sources provides an explanation of both bodies of data, and we demonstrate the feasibility of this approach with the computational model called COMPERE. We believe that this approach brings the language processing community significantly closer to offering human-like language processing systems.Comment: 7 pages, uses aaai.sty macr

Large-scale connectionist natural language parsing using lexical semantic and syntactic knowledge

Syntactic parsing plays a pivotal role in most automatic natural language processing systems. The research project presented in this dissertation has focused on two main characteristics of connectionist models for natural language processing: their adaptability to different tagging conventions, and their ability to use multiple linguistic constraints in parallel during sentence processing. In focusing on these key characteristics, an existing hybrid connectionist, shift-reduce corpus-based parsing model has been modified. This parser, which had earlier been trained to acquire linguistic knowledge from the Lancaster Parsed Corpus, has been adapted to learn linguistic knowledge from the Wall Street Journal Corpus. This adaptation is a novel demonstration that this connectionist parser, and by extension, other similar connectionist models, is able to adapt to more than one syntactic tagging convention; this implies their ability to adapt to the underlying linguistic theories used to annotate these corpora

Scaling connectionist compositional representations

The Recursive Auto-Associative Memory (RAAM) has come to dominate connectionist investigations into representing compositional structure. Although an adequate model when dealing with limited data, the capacity of RAAM to scale-up to real-world tasks has been frequently questioned. RAAM networks are difficult to train (due to the moving target effect) and as such training times can be lengthy. Investigations into RAAM have produced many variants in an attempt to overcome such limitations. We outline how one such model ((S)RAAM) is able to quickly produce context-sensitive representations that may be used to aid a deterministic parsing process. By substituting a symbolic stack in an existing hybrid parser, we show that (S)RAAM is more than capable of encoding the real-world data sets employed. We conclude by suggesting that models such as (S)RAAM offer valuable insights into the features of connectionist compositional representations.<br /

A Neural Network Architecture for Syntax Analysis

Artificial neural networks (ANNs), due to their inherent parallelism and potential fault tolerance, offer an attractive paradigm for robust and efficient implementations of syntax analyzers. This paper proposes a modular neural network architecture for syntax analysis on continuous input stream of characters. The components of the proposed architecture include neural network designs for a stack, a lexical analyzer, a grammar parser and a parse tree construction module. The proposed NN stack allows simulation of a stack of large depth, needs no training, and hence is not application-specific. The proposed NN lexical analyzer provides a relatively efficient and high performance alternative to current computer systems for lexical analysis especially in natural language processing applications. The proposed NN parser generates parse trees by parsing strings from widely used subsets of deterministic context-free languages (generated by LR grammars). The estimated performance of the proposed neural network architecture (based on current CMOS VLSI technology) for syntax analysis is compared with that of commonly used approaches to syntax analysis in current computer systems. The results of this performance comparison suggest that the proposed neural network architecture offers an attractive approach for syntax analysis in a wide range of practical applications such as programming language compilation and natural language processing