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An Overview of Studies on Automatic Genre Identification

Marina Santini

University of Brighton

UK

From Biberian Text Types to Genres of Web Pages

Université de Toulouse-Le Mirail, Maison de la rechercheToulouse, 5 et 6 octobre 2006

<http://w3.univ-tlse2.fr/erss/textes/seminaires/sc2006/sc2006.html>

GENRE TEXTUEL/DOMAINE/ACTIVITÉ

Journées d'étude organisées par l'opération «Sémantique et Corpus»

Overview of the Talk

Elusiveness of the concept of genreGenre & neighbouring termsCorpus-based approach & automatically-extractable featuresAutomatic text type identificationAutomatic genre classification

Electronic corporaCollections of web pages

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Elusiveness of the concept of genre

A codification of discursive properties (Todorov, 1978);

A social action (Miller, 1984);

A persuasive classifying statement (Rosmarin, 1985)

A pattern or a recurring type of text (Erickson, 1999)

An interface metaphor (Toms and Campbell, 1999);

A typified communicative action (Yates & Orlikoswki, 1992).

and so on…

Slippery but Intuitive

Academic papers

Fables

Editorials

Sonnets

Novels

Interviews

Letters

Recipies

Information Patient leaflets

Reviews

and so forth…

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Genre & neighbouring terms

Genre

Register

Text types

Domain

Topic

Folksonomies

[…]

Text Types, Style, Genre: Overlap

Klavans and Kan (1998)

Johannesson & Wallström (1999)

Karlgren (2000)

Stamatatos et al. (2001)

Dewdney et al. (2001)

Rehm (2006)

etc.

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Text Types, Genre, Domain & Topic:

Some DistinctionsText Types:

� Biberian text types: intimate interpersonal interaction, informational interaction, scientific exposition, etc.

� Rhetorical text types: narration, instruction, argumentation, etc.Genre:

� Text categories, e.g. news story, academic paper, interview, etc.Domain:

� Subject fields, e.g. religion, hobbies, etc.Topic:

� the content, i.e. what is the text about, e.g. Chirac, nuclear weapons, greenhouse effect, etc.

Genre, Register, Text Types, Domain, & Style, Lee (2001)

Corpus-based approach &

automatically-extractable features

Corpora: unsupervised approach vs. supervised approach

Features:limitationof automatically-extractable genre-revealing features

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Automatic Text Type Identification:

the Multi-Dimensional Analysis

Task: Language Analysis

Corpus-based techique

Unsupervised (bottom-up, inductive)A representative corpus (genres, registers, other categories)

Countable features

Factor analysis

Interpretation of the factors

Validation of the factors with statistical confirmitory techniques

Criticism: Lee (1999)

Biberian

Text TypesBiber (1988)Biber (1989)Biber (1993)Biber (1995)Biber (2004a)Biber (2004b)Biber et al. (2005)etc.

Genres/Registers/Other Categories

vs.

Text Types

External Features

vs.

Internal Features

“I have used the term ‘genre’ (or ‘register’) for text varieties that are readily recognized and ‘named’ within a culture (e.g. letters, press editorials, sermon,

conversation), while I have used the term ‘text type’ for varieties that are defined

linguistically (rather than perceptually)” (Biber, 1993).

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Multi-Dimensional Analysis

Factor Analysis, Factors Scores (Biber, 1988)Cluster Analysis (Biber, 1989)Additional Statistical Tests (Biber, 2004a; 2004b, etc.)

1. intimate interpersonal interaction

2. informational interaction

3. scientific exposition

4. learned exposition

5. imaginative narrative

6. general narrative exposition

7. situated reportage

8. involved persuasion

Cluster Analysis - Biber (1989)Factor 2 - Biber (1988)

Biberian features

“The notion of function is closely associated with the notion of situation. A primary motivation for analysis of the components of situtation is the desire to link the functions of particular linguistic features to variation in the communicative situation” (Biber, 1988: 33).

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Linguistic Features���� Text Types

Text types refer to groupings of texts that are similar with respect to their linguistic form, irrespective of genre/register/other categories.

Example: Conversation Text Types, Biber (2004a, JADT)

Microscopic Analysis + Macroscopic Analysis

Microscopic analysis is necessary to pinpoint the exact communicative functions of individual linguistic features.

Macroscopic analysis is needed to identify the underlying textual dimensions in a set of texts, enabling an overall account of linguistic variations among those texts.

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Text Type-Oriented Identification

Nakamura (1993)Takahashi (1997) Sigley (1997)

Yin and Power (2006)

TypTEXT (Illouz et al., 2000; Folch et al., 2000)TyPWEB (Beaudouin et al., 2001ab; Illouz & Habert 2002)

Automatic Genre Classification:

Electronic Corpora

Task: ClassificationCorpus-based

Supervised: discriminant analysis, logistic regression, classifiers (SVM, C4.5, Naive Bayes, Neural Network, etc.)

Corpus of documents labelled by genre

Countable features

Off-the-shelf statistical algorithms

Evaluation: cross-validation or test set

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From Biber’s text types to genres of electronic

corpora: Karlgren and Cutting (1994)

Karlgren and Cutting (1994): Recognizing Text Genres with Simple

Metrics Using Discriminant Analysis

20 featuresDiscriminant analysisBrown corpus

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Stamatatos et al. (2000):

Text Genre Detection Using Common Word Frequencies

50 common wordsDiscriminant analysisWall Street Journal Corpus

The multiThe multi--faceted approachfaceted approach: Kessler et al. (1997):Automatic Detection of Text Genre

Three categorial facets:Brow (popular, middle, upper-middle and high)

Narrative (narrative, non-narrative)

Genre (reportage, editorial, scitech, legal, non-fiction, or fiction)

A set of 55 lexical, character-level and derivative cues: very easy to extract!

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Summary

Two main tendencies:Descriptive Framework: Biber & multi-dimensional analysis

Classificatory Framework: Karlgren , Stamatatos, Kessler

In both frameworks:lack of an external reference corpuslack of an external reference corpus

Genre &

Layout/Shape

Dillon & Vaughan, 1997

Toms & Campbell, 1999a, 1999b

Bagdanov and Worring (2001a, 2001b)

Bagdanov (2004)

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Automatic Genre Classification:

Collections of Web Pages

Task: Classification of individual web pagesCorpus-based

Supervised: discriminant analysis, classifiers (SVM, Neural Networks, Timbl, etc.)

Corpus of web pages labelled by genre

Countable features

Off-the-shelf statistical algorithms

Evaluation: cross-validation or test set

Features for

Web PagesThe functionality attribute:

Html, URLs, other cues.

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16 Web Genres

Lim et al. (2005)

Authors using a supervised approach

on genre of individual web pages

Shepherd et al.(2004) Kennedy and Shepherd (2005) Lee and Myaeng (2002, 2004) Meyer zu Eissen and Stein (2004)Boese (2005)Lim et al. (2005)

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Analysing Hypertext Structure:

Genres of Websites

Academic’s personal home page: Rehm (2002, 2005, 2006)

Analysing Hypertext Structure:

Web Genre Representation for Corpus

Linguistic Studies

Websites as instances of web genres: Mehler & Gleim (2006)

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Summary

Main tendency:Use of supervised methods

“one web page = one genre”

Rehm, Mehler : HTML parser that understand the structure of the website

The website may include web pages with differing genres

My position:Automatic Identification of Genre in Automatic Identification of Genre in

Web PagesWeb Pages

a web page may have zero-, one-, or multiple genres, because of:

the complexity of web pages

the fluidity and the fast-paced evolution of the web

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Zero-, One-, Multiple Genres

Genre-Based Search Engines

Shepherd et al. (2004)Meyer zu Eissen & Stein (2004)Rehm (2005)etc.

(Rosso, 2005)

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Genre Retrieval and Visualization

Roussinov et al. (2001)Karlgren et al. (1998a, b) Dimitrova et al. (2002)

Genre & Domain/Topic

Wolters and Kirsten (1999): German, genre & domain

Poudat & Cleuziou (2003) : French, genre & domain

Pery-Woodley & Rebeyrolle (1998): French, feature patterns for retrieving definitional text across genres and domains

Lee and Mayeng (2002, 2004): English and Korean: can we use genre to help topical categorization?

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Clustering Genres and Topic

Rauber and Müller-Kögler (2001):

Digital Libraries

Genre as Polarities: Domain Transfer

Finn & Kusmerick (2006)

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Thank you for your attention…