Preface In this thesis, we introduce probabilistic models to rank the likelihood of resultant parses within the GLR parsing framework. Probabilistic models can also bring about the beneet of reduction of search space, if the models allow preex probabilities for partial parses. In devising the models, we carefully observe the nature of GLR parsing, one of the most eecient parsing algorithms in e...

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 ...

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...

There are two different approaches in the LR parsing. The first one is the deterministic approach that performs the only one action using the control rules learned without any LR parsing resource. It shows good performance in speed. But it has a disadvantage that it cannot correct the previous mistakes, thus directly affects the parsing result. The second one is the probabilistic LR parsing app...

We investigate hypothesis testing in nonparametric additive models estimated using simplified smooth backfitting (Huang and Yu, Journal of Computational Graphical Statistics, \textbf{28(2)}, 386--400, 2019). Simplified achieves oracle properties under regularity conditions provides closed-form expressions the estimators that are useful for deriving asymptotic properties. develop a generalized l...

This paper reviews and summarizes six diier-ent types of extra-grammatical phenomena and their corresponding recovery principles at the syntactic level, and describes some techniques used to deal with four of them completely within an Extended GLR parser (EGLR). Partial solutions to the remaining two by the EGLR parser are also discussed. The EGLR has been implemented.