Analyzing Holistic Parsers: Implications for Robust Parsing and Systematicity

Holistic parsers offer a viable alternative to traditional algorithmic parsers. They have good generalization performance and are robust inherently. In a holistic parser, parsing is achieved by mapping the connectionist representation of the input sentence to the connectionist representation of the target parse tree directly. Little prior knowledge of the underlying parsing mechanism thus needs to be assumed. However, it also makes holistic parsing difficult to understand. In this article, an analysis is presented for studying the operations of the confluent pre-order parser (CPP). In the analysis, the CPP is viewed as a dynamical system, and holistic parsing is perceived as a sequence of state transitions through its state-space. The seemingly one-shot parsing mechanism can thus be elucidated as a step-by-step inference process, with the intermediate parsing decisions being reflected by the states visited during parsing. The study serves two purposes. First, it improves our understanding of how grammatical errors are corrected by the CPP. The occurrence of an error in a sentence will cause the CPP to deviate from the normal track that is followed when the original sentence is parsed. But as the remaining terminals are read, the two trajectories will gradually converge until finally the correct parse tree is produced. Second, it reveals that having systematic parse tree representations alone cannot guarantee good generalization performance in holistic parsing. More important, they need to be distributed in certain useful locations of the representational space. Sentences with similar trailing terminals should have their corresponding parse tree representations mapped to nearby locations in the representational space. The study provides concrete evidence that encoding the linearized parse trees as obtained via preorder traversal can satisfy such a requirement.

[1]  Jordan B. Pollack,et al.  Recursive Distributed Representations , 1990, Artif. Intell..

[2]  Yves Schabes,et al.  Finite-State Approximation of Phrase-Structure Grammars , 1997 .

[3]  Eugene Santos,et al.  A Massively Parallel Self-Tuning Context-Free Parser , 1988, NIPS.

[4]  Eugene Charniak,et al.  Statistical language learning , 1997 .

[5]  Lonnie Chrisman,et al.  Learning Recursive Distributed Representations for Holistic Computation , 1991 .

[6]  Raymond L. Watrous,et al.  Induction of Finite-State Languages Using Second-Order Recurrent Networks , 1992, Neural Computation.

[7]  Bart Selman,et al.  Rule-Based Processing in a Connectionist System for Natural Language Understanding , 1985 .

[8]  Sandiway Fong,et al.  Natural Language Grammatical Inference with Recurrent Neural Networks , 2000, IEEE Trans. Knowl. Data Eng..

[9]  George Berg,et al.  A Connectionist Parser with Recursive Sentence Structure and Lexical Disambiguation , 1992, AAAI.

[10]  Jeffrey D. Ullman,et al.  Introduction to Automata Theory, Languages and Computation , 1979 .

[11]  Barry L. Kalman,et al.  Tail-recursive Distributed Representations and Simple Recurrent Networks , 1995 .

[12]  Morten H. Christiansen,et al.  Generalization and connectionist language learning , 1994 .

[13]  James F. Allen Natural language understanding , 1987, Bejnamin/Cummings series in computer science.

[14]  Jeffrey L. Elman,et al.  Finding Structure in Time , 1990, Cogn. Sci..

[15]  J. Fodor,et al.  Connectionism and cognitive architecture: A critical analysis , 1988, Cognition.

[16]  Kanaan A. Faisal,et al.  Symbolic parsing via subsymbolic rules , 1992 .

[17]  Lai-Wan Chan,et al.  Confluent Preorder Parsing of Deterministic Grammars , 1997, Connect. Sci..

[18]  Garrison W. Cottrell,et al.  Phase-Space Learning for Recurrent Networks , 1993 .

[19]  Geoffrey E. Hinton Tensor Product Variable Binding and the Representation of Symbolic Structures in Connectionist Systems , 1991 .

[20]  Ho Kei Shin Edward,et al.  Representing Sentence Structures on Neural Networks , 1994 .

[21]  Yves Schabes,et al.  Parsing with Finite-State Transducers , 1997 .

[22]  Chris Mellish,et al.  Natural Language Processing in Pop-11: An Introduction to Computational Linguistics , 1989 .

[23]  John F. Kolen,et al.  Exploring the computational capabilities of recurrent neural networks , 1995 .

[24]  James F. Allen Natural language understanding (2nd ed.) , 1995 .

[25]  Gerald Gazdar,et al.  Natural Language Processing in PROLOG: An Introduction to Computational Linguistics , 1989 .

[26]  Robert F. Hadley Systematicity in Connectionist Language Learning , 1994 .

[27]  Dana Angluin,et al.  Inductive Inference of Formal Languages from Positive Data , 1980, Inf. Control..

[28]  T. Gelder,et al.  On Being Systematically Connectionist , 1994 .

[29]  Geoffrey E. Hinton Learning and Applying Contextual Constraints in Sentence Comprehension , 1991 .

[30]  Risto Miikkulainen Subsymbolic Case-Role Analysis of Sentences with Embedded Clauses , 1993 .

[31]  Peter Tiño,et al.  Learning long-term dependencies in NARX recurrent neural networks , 1996, IEEE Trans. Neural Networks.

[32]  Ronan G. Reilly,et al.  Connectionist technique for on-line parsing , 1992 .

[33]  C. Lee Giles,et al.  Learning and Extracting Finite State Automata with Second-Order Recurrent Neural Networks , 1992, Neural Computation.

[34]  Ajay N. Jain Parsing Complex Sentences with Structured Connectionist Networks , 1991, Neural Computation.

[35]  Paul Rodríguez,et al.  A Recurrent Neural Network that Learns to Count , 1999, Connect. Sci..

[36]  Lai-Wan Chan,et al.  How to Design a Connectionist Holistic Parser , 1999, Neural Computation.

[37]  Mark A. Fanty,et al.  Context-free parsing with connectionist networks , 1987 .