Analysis-oriented two-level grammars

Summary: p. 2-3

Experiences with the GTU grammar development environment

In this paper we describe our experiences with a tool for the development and testing of natural language grammars called GTU (German: Grammatik-Testumgebumg; grammar test environment). GTU supports four grammar formalisms under a window-oriented user interface. Additionally, it contains a set of German test sentences covering various syntactic phenomena as well as three types of German lexicons that can be attached to a grammar via an integrated lexicon interface. What follows is a description of the experiences we gained when we used GTU as a tutoring tool for students and as an experimental tool for CL researchers. From these we will derive the features necessary for a future grammar workbench.Comment: 7 pages, uses aclap.st

Code generation using a backtracking LR parser

Although the parsing phase of the modern compiler has been automated in a machine independent fashion, the diversity of computer architectures inhibits automating the code generation phase. During code generation, some intermediate representation of a source program is transformed into actual machine instructions. The need for portable compilers has driven research towards the automatic generation of code generators.;This research investigates the use of a backtracking LR parser that treats code generation as a series of tree transformations

Handling unknown words in statistical latent-variable parsing models for Arabic, English and French

This paper presents a study of the impact of using simple and complex morphological clues to improve the classification of rare and unknown words for parsing. We compare this approach to a language-independent technique often used in parsers which is based solely on word frequencies. This study is applied to three languages that exhibit different levels of morphological expressiveness: Arabic, French and English. We integrate information about Arabic affixes and morphotactics into a PCFG-LA parser and obtain stateof-the-art accuracy. We also show that these morphological clues can be learnt automatically from an annotated corpus