A Flexible Example-Based Parser Based on the SSTC

A FLEXIBLE EXAMPLE-BASED PARSER BASED ON THE SSTC" Mosleh Hmoud A1-Adhaileh & Tang Enya Kong Computer Aided Translation Unit School of computer sciences University Sains Malaysia 1 1800 PENANG, MALAYSIA mosleh @ cs. usm.my, enyakong @ cs. usm. my Abstract In this paper we sketch an approach for Natural Language parsing. Our approach is an example-based approach, which relies mainly on examples that already parsed to their representation structure, and on the knowledge that we can get from these examples the required information to parse a new input sentence. In our approach, examples are annotated with the Structured String Tree Correspondence (SSTC) annotation schema where each SSTC describes a sentence, a representation tree as well as the correspondence between substrhzgs in the sentence and subtrees in the representation tree. In the process of parsing, we first try to build subtrees for phrases in the input sentence which have been successfully found in the example-base - a bottom up approach. These subtrees will then be combined together to form a single rooted representation tree based on an example with similar representation structure - a top down approach. Keywords: Example-based parsing, SSTC. 1. INTRODUCTION In natural language processing (NLP), one key problem is how to design an effective parsing system. Natural language parsing is the process of analyzing or parsing that takes sentences in a natural language and converts them to some representation form suitable for further interpretation towards some applications might be required, for example, translation, text abstraction, question-answering, etc. The generated representation tree structure can be a phrase structure tree, a dependency tree or a logical structure tree, as required by the application involved. Here we design an approach for parsing natural language to its representation structure, which depends on related examples already parsed in the example-base. This approach is called example-based parsing, as oppose to the traditional approaches of natural language parsing which normally are based on rewriting rules. Here linguistic knowledge extracted directly from the example-base will be used to parse a natural language sentence (i.e. using past language experiences instead of rules). For a new sentence, to build its analysis (i.e. representation structure tree), ideally if the sentence is already in the example-base, its analysis is found there too, but in general, the input sentence will not be found in the example-base. In such case, a method is used to retrieve close related examples and use the knowledge from these examples to build the analysis for the input sentence. In general, this approach relies on the assumption that if two strings (phrase or sentence) are "close", their analysis should be "close" too. If the analysis of the first one is known, the analysis of the other can be obtained by making some modifications in the analysis of the first one. The example-based approach has become a common technique for NLP applications, especially in MT as reported in [1], [2] or [3]. However, a main problem normally arises in the current approaches which indirectly limits their applications in …

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