STYLOMETRY IN IR SYSTEMS

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STYLOMETRY IN IR SYSTEMS

• Leyla BILGE
• Büsra ÇELIKKAYA
• Kardelen HATUN

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Outline

• Stylistics and Stylometry
• Applications of stylometry
• History of stylometric researches
• Stylistic features
• Recent Studies
• Our approach
• Conclusion

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STYLISTICS

• The theoritical framework for stylistic combines
• Hallidays Language Theory
• Sanders Theories of Stylistic
• Halliday says
• A text is what is meant, selected from the
  total set of opinions that constitute what can be
  meant
• Sander says
• Style is the result of choices made by an
  author from a range of possibilities offered by
  the language system

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STYLISTICS

• Stylistic variation depends on
• Author preferences and competence
• Familiarity
• Genre
• Communicative context
• Expected characteristics of the intended audience
  
• Modeling, representing and utilizing this
  variation is the business of stylistic analysis.

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stylometry

• The application of the study of linguistic style
• Style refers to the linguistic choices of authors
  that persist over their works, independently of
  content
• Aim is to describe a text from a rather formal
  perspective like
• Number of words
• Number of repetitions
• Sentence length

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APPLICATIONS OF STYLOMETRY

• Authorship attribution
• Forensic author identification
• To find the author of an anonymous text
• Observation of the characteristics of a
  particular author
• Organization and retrieval of documents based on
  their writing style
• Systems for genre-based information retrieval

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HISTORY OF STYLOMETRY

• Stylometry grew out of analyzing text for
  evidence of authenticity, authorial identity
• According to modern practice of discipline, there
  are distinctive patterns of a language to
  identify authors
• After development of computers and their
  capacities
• Large data sets can be analyzed
• New methods can be generated and easily applied

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HISTORY OF STYLOMETRY, CONTD

• Current researches uses techniques based on term
  frequency counts
• Frequency data are collected for common terms
• These data are then analyzed using a range of
  fairly standard statistical techniques
• However, they cannot guarantee quality ouput yet,
  i.e. Ulysses

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Methodology

• Use a subset of structural and stylometric
  features on a set of authors without
  consideration of author characteristics
• Currently, authorship attribution studies are
  dominated by the use of lexical measures
• Generally used statistics
• Word length
• Syllables per word
• Sentence-length
• Sentence count
• Text length in words
• Use of punctuation marks

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Stylistic Features

• Lexically-Based Methods
• Vocabulary richness of the author
• Frequencies of occurrence of individual words
• Vocabulary diversity
• Type-token ratio V/N
• V size of vocabulary of sample text
• N number of tokens
• Hapax legomena
• How many words occur once
• Frequencies of occurrence
• Function words

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Stylistic Features

• Problems
• Text length dependent
• Unstable for short texts
• Function word set requires manual effort
• Specific to the group of authors considered
• Solution
• Use set of most frequent words
• Both content-words and function words

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Related Studies

• Analysis of the text by a natural language
  processing tool
• Use existing NLP tool
• Sentence and Chunk Boundaries Detector (SCBD)
• Use sub-word units like character N-grams instead
  of word frequencies
• Character sequences of length n
• Most frequent n-grams provide information about
  authors stylistic choices on lexical,
  syntactical and structural level

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Word based features

• Bag-of-words
• Apply stemming and stopword list
• Function words
• Content-free
• POS Annotation
• Feature Selection
• Semantic Disambiguation

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Linguistic constituents

• Structure of natural language sentences show word
  occurrences follow a specific order
• Words are grouped into syntactic units called
  constituents
• Use word relationships by extracting constituents
  for feature construction
• Subdivide document into sentences
• Construct a syntax tree for each sentence

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Syntax tree

• Use a syntax tree representation of different
  authors sentences as features

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Our Aprroach

• Use Stylometry to analyze the following
• Texts translated by the same translator but
  written by different authors
• Texts translated by different translators but
  written by the same authors

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Proposed Steps

• Feature Extraction
• Determine which features represent the style best
• Training
• Training the classifier with a training set
• Many methods present, (SVM, bayesian)
• Recognition and Classification of texts
• Analyzing the results of classification

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1. Feature Extraction

• The stylometric features of a text can be
• Word length
• Sentence length
• Paragraph length
• Character n-grans
• Function words
• Feature choices affect classification results
  seriously.
• Then obtain a feature vector with n-dimensions
• V v1,v2,v3 vn

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2. Training

• Choose training data for every class
• May be randomly selected texts
• May be manually picked
• Determine the corresponding parameters to each
  class

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3. Recognition and Classification

• Use the parameters we obtained from training data
• Compute the distance
• Label the data
• Classify the data

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Results of the Classification

• We will have two set of results
• The original texts classified by author
• The translated texts classified by no prior class
  information
• These results will give us a clue about the two
  issues we stated at the beginning
• Example The Picture of Dorian Gray is
  translated into Turkish by many translators
• Look if these are clustered in one class or
  separate classes

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Our Aim

• With the right classification we will be able to
  identify
• If sytlometric analysis works in finding an
  author in two different languages
• If translations carry more of their translators
  style or if they still have their authors style
• yet, to date, no stylometrist has managed to
  establish a methodology which is better able to
  capture the style of a text than that based on
  lexical items.

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Conclusion

• Today there are many useful applications of
  stylometry.
• Authorship attribution, plagiarism detection,
  genre-based information retrieval
• What features are valuable for analysis is still
  an important question.
• We aim to find the stylistic connection between a
  text and its translation.

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References

• Computational Stylistics in Forensic Author
  Identifiction, Carole E. Charsi
• Style vs. Expression in Literary Narratives,
  Özlem Uzuner, Boris Katz
• Computer-Based Authorship Attribution Without
  Lexical Measures, E. Stamatatos, N. Fakotakis, G.
  Kokkinakis
• Ensemble-Based Author Identification Using
  Character N-grams, E. Stamatatos
• Combining Text and Linguistic Document
  Representations for Authorship Attribution, A.
  Kaster, S. Siersdofer, G. Weikum

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