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.

We are very grateful to the Editors, Maria Angeles Gil and Leandro Pardo, for organizing this stimulating discussion. We would like to take this opportunity to thank all discussants for their insightful and constructive comments regarding our paper, opening new avenues for the GLR tests. They have made valuable contributions to the understanding of various testing problems. As stressed in our p...