scholarly journals Incremental Predictive Parsing with TurboParser

2014 ◽  
Author(s):  
Arne Köhn ◽  
Wolfgang Menzel
Keyword(s):  
Predictive Parsing

scholarly journals Aplikasi Piranti Lunak Penganalisis Sintaksis Kalimat Bahasa Indonesia

2011 ◽  
Vol 2 (1) ◽  
pp. 80
Author(s):  
Sablin Yusuf
Keyword(s):  
Rapid Prototyping ◽  
Regular Expression ◽  
Requirement Analysis ◽  
Syntax Analysis ◽  
Software Application ◽  
Analysis Process ◽  
System Requirement ◽  
Analysis Design ◽  
Predictive Parsing ◽  
Bahasa Indonesia

Indonesian has a vast variety in structures and sentence types that brings about difficulty in using the language correctly and accurately. This research aims for producing a software application for syntax analysis and error checking for Indonesian sentences. The method used in developing the application is rapid prototyping method which consists of several phases: system requirement analysis, design, coding, implementation and evaluation while the method used in syntax analysis process is regular expression and predictive parsing method. Despite some existing limitations, the application produced is able to check the syntax error of various Indonesian sentence types. 

Predictive Parsing

1984 ◽  
pp. 98-98
Author(s):  
Alan Bundy ◽  
Lincoln Wallen
Keyword(s):  
Predictive Parsing

scholarly journals Interactive predictive parsing

2009 ◽  
Author(s):  
Ricardo Sánchez-Sáez ◽  
Joan-Andreu Sánchez ◽  
José-Miguel Benedí
Keyword(s):  
Predictive Parsing

scholarly journals Incremental, Predictive Parsing with Psycholinguistically Motivated Tree-Adjoining Grammar

2013 ◽  
Vol 39 (4) ◽  
pp. 1025-1066 ◽  
Author(s):  
Vera Demberg ◽  
Frank Keller ◽  
Alexander Koller
Keyword(s):  
Eye Tracking ◽  
Syntactic Structure ◽  
Psycholinguistic Research ◽  
Partial Structures ◽  
Tree Adjoining Grammar ◽  
Predictive Parsing

Psycholinguistic research shows that key properties of the human sentence processor are incrementality, connectedness (partial structures contain no unattached nodes), and prediction (upcoming syntactic structure is anticipated). There is currently no broad-coverage parsing model with these properties, however. In this article, we present the first broad-coverage probabilistic parser for PLTAG, a variant of TAG that supports all three requirements. We train our parser on a TAG-transformed version of the Penn Treebank and show that it achieves performance comparable to existing TAG parsers that are incremental but not predictive. We also use our PLTAG model to predict human reading times, demonstrating a better fit on the Dundee eye-tracking corpus than a standard surprisal model.

scholarly journals Depth Embedded Recurrent Predictive Parsing Network for Video Scenes

2019 ◽  
Vol 20 (12) ◽  
pp. 4643-4654
Author(s):  
Lingli Zhou ◽  
Haofeng Zhang ◽  
Yang Long ◽  
Ling Shao ◽  
Jingyu Yang
Keyword(s):  
Predictive Parsing
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