Transcript STYLOMETRY IN IR SYSTEMS Leyla BİLGE Büşra ÇELİKKAYA

STYLOMETRY IN IR
SYSTEMS
Leyla BİLGE
Büşra ÇELİKKAYA
Kardelen HATUN
OUTLINE
4/20/2007
Stylistics and Stylometry
 Applications of stylometry
 History of stylometric researches
 Stylistic features
 Recent Studies
 Our approach
 Conclusion
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Stylometry in IR Systems
STYLISTICS
The theoritical framework for stylistic combines;
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Halliday’s Language Theory
Sander’s Theories of Stylistic
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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
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Author preferences and competence
Familiarity
Genre
Communicative context
Expected characteristics of the intended audience
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Modeling, representing and utilizing this
variation is the business of stylistic analysis.
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STYLOMETRY
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The application of the study of linguistic style
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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;
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Number of words
Number of repetitions
Sentence length
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APPLICATIONS OF STYLOMETRY
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Authorship attribution
Forensic author identification
 To find the author of an anonymous text
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Stylometry in IR Systems
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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
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Stylometry grew out of analyzing text for
evidence of authenticity, authorial identity
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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, CONT’D
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Stylometry in IR Systems
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
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However, they cannot guarantee quality ouput
yet, i.e. Ulysses
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METHODOLOGY
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Use a subset of structural and stylometric features on
a set of authors without consideration of author
characteristics
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Currently, authorship attribution studies are
dominated by the use of lexical measures
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Generally used statistics:
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Word length
Syllables per word
Sentence-length
Sentence count
Text length in words
Use of punctuation marks
STYLISTIC FEATURES
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Lexically-Based Methods
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Vocabulary richness of the author
Frequencies of occurrence of individual words
Vocabulary diversity:
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Type-token ratio V/N
V: size of vocabulary of sample text
 N: number of tokens
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Hapax legomena
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How many words occur once
Frequencies of occurrence:
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Function words
STYLISTIC FEATURES
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Problems:
Text length dependent
 Unstable for short texts
 Function word set requires manual effort
 Specific to the group of authors considered
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Solution:
Use set of most frequent words
 Both content-words and function words
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RELATED STUDIES
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Analysis of the text by a natural language
processing tool:
Use existing NLP tool
 Sentence and Chunk Boundaries Detector (SCBD)
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Use sub-word units like character N-grams
instead of word frequencies:
Character sequences of length n
 Most frequent n-grams provide information about
author’s stylistic choices on lexical, syntactical and
structural level
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WORD BASED FEATURES
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Bag-of-words
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Apply stemming and stopword list
Function words
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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
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Subdivide document into sentences
 Construct a syntax tree for each sentence
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SYNTAX TREE
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Use a syntax tree representation of different
authors sentences as features
OUR APRROACH
Use Stylometry to
analyze the following
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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
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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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Training
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Determine which features represent the style best
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Feature Extraction
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1. FEATURE EXTRACTION
The stylometric features of a text can be:
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Feature choices affect classification results
seriously.
 Then obtain a feature vector with n-dimensions
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Word length
Sentence length
Paragraph length
Character n-grans
Function words
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V = {v1,v2,v3 … vn}
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2. TRAINING
Choose training data
for every class
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Determine the
corresponding
parameters to each
class
Feature
Extraction
Class
Parameters
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May be randomly
selected texts
 May be manually
picked
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Training
data
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3. RECOGNITION AND CLASSIFICATION
Distance
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Recognition
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Use the parameters
we obtained from
training data
 Compute the distance
 Label the data
 Classify the data
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Classification
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RESULTS OF THE CLASSIFICATION
We will have two set of results
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These results will give us a clue about the two
issues we stated at the beginning
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Example: “The Picture of Dorian Gray” is translated
into Turkish by many translators
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The original texts classified by author
 The translated texts classified by no prior class
information
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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
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“…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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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
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CONCLUSION
Today there are many useful applications of
stylometry.
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Authorship attribution, plagiarism detection, genrebased information retrieval
What features are valuable for analysis is still an
important question.
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We aim to find the stylistic connection between a
text and its translation.
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REFERENCES
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Stylometry in IR Systems
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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