PNEPs, NEPs for context free parsing: Application to natural language processing

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-02478-8_59Proceedings of 10th International Work-Conference on Artificial Neural Networks, IWANN 2009, Salamanca, Spain.This work tests the suitability of NEPs to parse languages. We propose PNEP, a simple extension to NEP, and a procedure to translate a grammar into a PNEP that recognizes the same language. These parsers based on NEPs do not impose any additional constrain to the structure of the grammar, which can contain all kinds of recursive, lambda or ambiguous rules. This flexibility makes this procedure specially suited for Natural Languge Processing (NLP). In a first proof with a simplified English grammar, we got a performance (a linear time complexity) similar to that of the most popular syntactic parsers in the NLP area (Early and its derivatives). All the possible derivations for ambiguous grammars were generatedThis work was partially supported by MEC, project TIN2008-02081/TIN and by DGUI CAM/UAM, project CCG08-UAM/TIC-4425

Parsing of Hyperedge Replacement Grammars with Graph Parser Combinators

Graph parsing is known to be computationally expensive. For this reason the construction of special-purpose parsers may be beneficial for particular graph languages. In the domain of string languages so-called parser combinators are very popular for writing efficient parsers. Inspired by this approach, we have proposed graph parser combinators in a recent paper, a framework for the rapid development of special-purpose graph parsers. Our basic idea has been to define primitive graph parsers for elementary graph components and a set of combinators for the flexible construction of more advanced graph parsers. Following this approach, a declarative, but also more operational description of a graph language can be given that is a parser at the same time. In this paper we address the question how the process of writing correct parsers on top of our framework can be simplified by demonstrating the translation of hyperedge replacement grammars into graph parsers. The result are recursive descent parsers as known from string parsing with some additional nondeterminism

Parsing of Adaptive Star Grammars

In a recent paper, adaptive star grammars have been proposed as an extension of node and hyperedge replacement grammars. A rule in an adaptive star grammar is actually a rule schema which, via the so-called cloning operation, yields a potentially infinite number of concrete rules. Adaptive star grammars are motivated by application areas such as modeling and refactoring object-oriented programs, and they are more powerful than node and hyperedge replacement grammars by this mechanism. It has been shown that the membership problem is decidable for a reasonably large subclass of adaptive star grammars, however no parser has been proposed. This paper describes such a parser for this subclass motivated by the well-known string parser by Cocke, Younger, and Kasami

Hypergraph-Based Recognition Memory Model for Lifelong Experience

Cognitive agents are expected to interact with and adapt to a nonstationary dynamic environment. As an initial process of decision making in a real-world agent interaction, familiarity judgment leads the following processes for intelligence. Familiarity judgment includes knowing previously encoded data as well as completing original patterns from partial information, which are fundamental functions of recognition memory. Although previous computational memory models have attempted to reflect human behavioral properties on the recognition memory, they have been focused on static conditions without considering temporal changes in terms of lifelong learning. To provide temporal adaptability to an agent, in this paper, we suggest a computational model for recognition memory that enables lifelong learning. The proposed model is based on a hypergraph structure, and thus it allows a high-order relationship between contextual nodes and enables incremental learning. Through a simulated experiment, we investigate the optimal conditions of the memory model and validate the consistency of memory performance for lifelong learning

Parsing String Generating Hypergraph Grammars

A string generating hypergraph grammar is a hyperedge replacement grammar where the resulting language consists of string graphs i.e. hypergraphs modeling strings. With the help of these grammars, string languages like a n b n c n can be modeled that can not be generated by context-free grammars for strings. They are well suited to model discontinuous constituents in natural languages, i.e. constituents that are interrupted by other constituents. For parsing context-free Chomsky grammars, the Earley parser is well known. In this paper, an Earley parser for string generating hypergraph grammars is presented, leading to a parser for natural languages that is able to handle discontinuities

String Generating Hypergraph Grammars with Word Order Restrictions

Discontinuous constituents and free word order pose constant problems in natural language parsing. String generating hypergraph grammars have been proven useful for handling discontinuous constituents. In this paper we describe a new notation for hypergraph productions that allows on-the-fly interconnection of graph parts with regard to user-defined constraints. These constraints handle the order of nodes within the string hypergraph. The HyperEarley parser for string generating hypergraph grammars [1] is adapted to the new formalism. A German example is used for the explanation of the new notation and algorithms

String Generating Hypergraph Grammars with Word Order Restrictions

Abstract. Discontinuous constituents and free word order pose constant problems in natural language parsing. String generating hypergraph grammars have been proven useful for handling discontinuous constituents. In this paper we describe a new notation for hypergraph productions that allows on-the-fly interconnection of graph parts with regard to user-defined constraints. These constraints handle the order of nodes within the string hypergraph. The HyperEarley parser for string generating hypergraph grammars [1] is adapted to the new formalism. A German example is used for the explanation of the new notation and algorithms. 1 Free Word Order in Natural Languages A prominent difference between natural languages lies the order of words or constituents (groups of words that belong together on a syntactic level) in the various types of sentences. E.g. declarative sentences in English have a fixed word order with the subject first, followed by the verb, the objects and finall