Predicting Garden Path Sentences Based on Natural Language Understanding System

Natural language understanding (NLU) focusing on machine reading comprehension is a branch of natural language processing (NLP). The domain of the developing NLU system covers from sentence decoding to text understanding and the automatic decoding of GP sentence belongs to the domain of NLU system. GP sentence is a special linguistic phenomenon in which processing breakdown and backtracking are two key features. If the syntax-based system can present the special features of GP sentence and decode GP sentence completely and perfectly, NLU system can improve the effectiveness and develop the understanding skill greatly. On the one hand, by means of showing Octav Popescu's model of NLU system, we argue that the emphasis on the integration of syntactic, semantic and cognitive backgrounds in system is necessary. On the other hand, we focus on the programming skill of IF-THEN-ELSE statement used in N-S flowchart and highlight the function of context free grammar (CFG) created to decode GP sentence. On the basis of example-based analysis, we reach the conclusion that syntax- based machine comprehension is technically feasible and semantically acceptable, and that N-S flowchart and CFG can help NLU system present the decoding procedure of GP sentence successfully. In short, syntax-based NLU system can bring a deeper understanding of GP sentence and thus paves the way for further development of syntax-based natural language processing and artificial intelligence.

[1]  Farid Nouioua,et al.  From the textual description of an accident to its causes , 2009, Artif. Intell..

[2]  Peter Ford Dominey,et al.  A cognitive neuroscience perspective on embodied language for human–robot cooperation , 2010, Brain and Language.

[3]  Yong-Seok Choi,et al.  Building an automated English sentence evaluation system for students learning English as a second language , 2011, Comput. Speech Lang..

[4]  Emmanuel J. Yannakoudakis,et al.  n-Grams and their implication to natural language understanding , 1990, Pattern Recognit..

[5]  Kenneth R. Koedinger,et al.  Logic-based natural language understanding in intelligent tutoring systems , 2005 .

[6]  Yu Ping-fang,et al.  A computational linguistic approach to natural language processing with applications to garden path sentences analysis , 2012 .

[7]  Gary Geunbae Lee,et al.  Machine Learning Approaches to Spoken Language Understanding for Ambient Intelligence , 2010, AmI 2010.

[8]  Kim Kirsner,et al.  Objective measurement of fluency in natural language production: A dynamic systems approach , 2010, Journal of Neurolinguistics.

[9]  Ramón López-Cózar,et al.  Using Knowledge about Misunderstandings to Increase the Robustness of Spoken Dialogue Systems , 2010, TSD.

[10]  Abe Lockman,et al.  The control of inferencing in natural language understanding , 1983 .

[11]  Abdel-Hameed A. Badawy.  Students Perceptions of the Effectiveness of Discussion Boards What can we get from our students for a freebie point , 2012, ArXiv.

[12]  Johan Bos,et al.  Applying automated deduction to natural language understanding , 2009, J. Appl. Log..

[13]  Roger C. Schank,et al.  Conceptual dependency: A theory of natural language understanding , 1972 .

[14]  Y. Wilks.  A position note on natural language understanding and artificial intelligence , 1981, Cognition.

[15]  Feng Gao,et al.  Spoken language understanding using weakly supervised learning , 2010, Comput. Speech Lang..

[16]  B. Shneiderman,et al.  Flowchart techniques for structured programming , 1973, SIGP.

[17]  Gary Geunbae Lee,et al.  Practical use of non-local features for statistical spoken language understanding , 2008, Comput. Speech Lang..

[18]  Ping-fang Yu,et al.  Syntax-Directed Machine Translation of Natural Language: Effect of Garden Path Phenomenon on Sentence Structure , 2010, 2010 International Conference on Intelligent System Design and Engineering Application.

[19]  Wolfgang Minker,et al.  Design considerations for knowledge source representations of a stochastically-based natural language understanding component , 1999, Speech Commun..

[20]  Jeff Z. Pan,et al.  Natural language directed inference from ontologies , 2008, Artif. Intell..