A Deep Context Grammatical Model For Authorship Attribution
We define a variable-order Markov model, representing a Probabilistic Context Free Grammar, built from the sentence-level, de-lexicalized parse of source texts generated by a standard lexicalized parser, which we apply to the authorship attribution task. First, we motivate this model in the context of previous research on syntactic features in the area, outlining some of the general strengths and limitations of the overall approach. Next we describe the procedure for building syntactic models for each author based on training cases. We then outline the attribution process - assigning authorship to the model which yields the highest probability for the given test case. We demonstrate the efficacy for authorship attribution over different Markov orders and compare it against syntactic features trained by a linear kernel SVM. We find that the model performs somewhat less successfully than the SVM over similar features. In the conclusion, we outline how we plan to employ the model for syntactic evaluation of literary texts.
Code (0)
등록된 구현이 없습니다.
Tasks
Authorship AttributionmodelSentenceSimilar Papers 제목 키워드 기반
Authorship Attribution through Function Word Adjacency Networks
A method for authorship attribution based on function word adjacency networks (WANs) is introduced. Function words are parts of speech that express grammatical relationships between other words but do not carry lexical m…
Authorship AttributionMode Effects' Challenge to Authorship Attribution
The success of authorship attribution relies on the presence of linguistic features specific to individual authors. There is, however, limited research assessing to what extent authorial style remains constant when indiv…
Authorship AttributionFeature EngineeringSentenceDocument Author Classification Using Parsed Language Structure
Over the years there has been ongoing interest in detecting authorship of a text based on statistical properties of the text, such as by using occurrence rates of noncontextual words. In previous work, these techniques h…
ClassificationAvengers Ensemble! Improving Transferability of Authorship Obfuscation
Stylometric approaches have been shown to be quite effective for real-world authorship attribution. To mitigate the privacy threat posed by authorship attribution, researchers have proposed automated authorship obfuscati…
Authorship AttributionPatch-Based Spatial Authorship Attribution in Human-Robot Collaborative Paintings
As agentic AI becomes increasingly involved in creative production, documenting authorship has become critical for artists, collectors, and legal contexts. We present a patch-based framework for spatial authorship attrib…