Evaluating GLR parsing algorithms

Eager Glr Parsing

Faster Scannerless GLR Parsing

Analysis and renovation of large software portfolios requires syntax analysis of multiple, usually embedded, languages and this is beyond the capabilities of many standard parsing techniques. The traditional separation between lexer and parser falls short due to the limitations of tokenization based on regular expressions when handling multiple lexical grammars. In such cases scannerless parsin...

A New Formalization of Probabilistic GLR Parsing

This paper presents a new formalization of probabilistic GLR language modeling for statistical parsing. Our model inherits its essential features from Briscoe and Carroll's generalized probabilistic LR model [3], which obtains context-sensitivity by assigning a probability to each LR parsing action according to its left and right context. Briscoe and Carroll's model, however, has a drawback in ...

Empirical Support for Probabilistic GLR Parsing

This paper discusses the e ectiveness of a new probabilistic generalized LR model (PGLR) in word-based parsing (morphological and syntactic analysis) tasks, in which we have to consider the word segmentation and multiple part-of-speech problems. Parsing a sentence from the morphological level makes the task much more complex because of the increase of parse ambiguity stemming from word segmenta...

Empirical Evaluation of Probabilistic GLR Parsing

This paper presents the results of experiments on probabilistic GLR (PGLR), a new probabilistic model proposed by (Inui et al., 1997). The model is formalized based on stack transition during parsing distinguishing it from the existing models proposed byWright andWrigley, and Briscoe and Carroll. Our model produces a remarkable improvement in syntactic parsing with probability. Associating prob...