Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  define a variable-order Markov model, representing a Probabilistic Context Free Grammar, built from the sentence-level, delexicalized#R##N#parse of source texts generated by a standard lexicalized parser, which  apply to the authorship attribution task. First, #R##N#motivate this model in the context of previous research on syntactic features in the area, outlining some of the general strengths and#R##N#limitations of the overall approach. Next  describe the procedure for building syntactic models for each author based on training#R##N#cases.  then outline the attribution process – assigning authorship to the model which yields the highest probability for the given#R##N#test case.  demonstrate the efficacy for authorship attribution over different Markov orders and compare it against syntactic features#R##N#trained by a linear kernel SVM.  find that the model performs somewhat less successfully than the SVM over similar features. In the#R##N#conclusion,  outline how  plan to employ the model for syntactic evaluation of literary texts.
['Simon Fuller', 'Phil Maguire', 'Philippe Moser']
I am writing a research paper on a idea A Deep Context Grammatical Model For Authorship Attribution
[{"from": "human", "value": "I am writing a research paper on a idea A Deep Context Grammatical Model For Authorship Attribution"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  define a variable-order Markov model, representing a Probabilistic Context Free Grammar, built from the sentence-level, delexicalized#R##N#parse of source texts generated by a standard lexicalized parser, which  apply to the authorship attribution task. First, #R##N#motivate this model in the context of previous research on syntactic features in the area, outlining some of the general strengths and#R##N#limitations of the overall approach. Next  describe the procedure for building syntactic models for each author based on training#R##N#cases.  then outline the attribution process \u2013 assigning authorship to the model which yields the highest probability for the given#R##N#test case.  demonstrate the efficacy for authorship attribution over different Markov orders and compare it against syntactic features#R##N#trained by a linear kernel SVM.  find that the model performs somewhat less successfully than the SVM over similar features. In the#R##N#conclusion,  outline how  plan to employ the model for syntactic evaluation of literary texts."}]