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Genres on the Web Computational Models and Empirical Studies

:	Alexander Mehler, Serge Sharoff, Marina Santini
:	Alexander Mehler, Serge Sharoff, Marina Santini
:	Genres on the Web Computational Models and Empirical Studies
:	Springer-Verlag
:	9789048191789
:	1
:	CHF 135.40
:

:	Anwendungs-Software
:	English

:	362
:	Wasserzeichen/DRM
:	PC/MAC/eReader/Tablet
:	PDF

The volume 'Genres on the Web' has been designed for a wide audience, from the expert to the novice. It is a required book for scholars, researchers and students who want to become acquainted with the latest theoretical, empirical and computational advances in the expanding field of web genre research. The study of web genre is an overarching and interdisciplinary novel area of research that spans from corpus linguistics, computational linguistics, NLP, and text-technology, to web mining, webometrics, social network analysis and information studies. This book gives readers a thorough grounding in the latest research on web genres and emerging document types.

The book covers a wide range of web-genre focused subjects, such as:
• The identification of the sources of web genres
• Automatic web genre identification
• The presentation of structure-oriented models
• Empirical case studies

One of the driving forces behind genre research is the idea of a genre-sensitive information system, which incorporates genre cues complementing the current keyword-based search and retrieval applications.

	Foreword	6
	Personal Note	9
	Contents	10
	Contributors	12
	Part I Introduction	14
	1 Riding the Rough Waves of Genre on the Web	15
	1.1 Why Is Genre Important?	15
	1.1.1 Zooming In: Information on the Web	16
	1.2 Trying to Grasp the Ungraspable?	18
	1.2.1 In Quest of a Definition of Web Genre for Empirical Studies and Computational Applications	20
	1.3 Empirical and Computational Approaches to Genre: Open Issues	21
	1.3.1 Web Documents	21
	1.3.2 Corpora, Genres and the Web	26
	1.3.3 Empirical and Computational Models of Web Genres	30
	1.4 Conclusions	34
	1.5 Outline of the Volume	35
	References	37
	Part II Identifying the Sources of Web Genres	43
	2 Conventions and Mutual Expectations	44
	2.1 Genres Are Not Rule-Bound	44
	2.2 So, Let's Ask the Readers	46
	2.3 An Editorial, Third Party, View of Genres on the Web	51
	2.4 Data Source: Observation of User Actions	53
	2.5 Conclusions	56
	References	56
	3 Identification of Web Genres by User Warrant	58
	3.1 Introduction	58
	3.2 Criteria for the Identification of Web Genre	60
	3.3 Operationalizing Traditional Genre Theory for the World Wide Web	61
	3.3.1 A Genre's User Group	61
	3.3.2 Genre: Function, Form and Substance	63
	3.3.3 Genres on the Web: Further Implications for Research	66
	3.4 Developing a Web Genre Palette	66
	3.4.1 Collecting Genre Terminology in the Users' Own Words	67
	3.4.2 Users Choose the Best of the Collected Genre Terminology	69
	3.4.3 User Validation of the Genre Palette	72
	3.4.4 A Fourth Study: Determining the Genres' Usefulness for Web Search	75
	3.5 Conclusion	76
	References	77
	4 Problems in the Use-Centered Development of a Taxonomy of Web Genres	79
	4.1 Introduction	79
	4.1.1 What Is the Purpose of a Genre Taxonomy?	80
	4.2 Why Is It Hard to Develop a Web Genre Taxonomy?	81
	4.2.1 Difficulties in Defining Genres	81
	4.2.2 Difficulties in Developing the Scope and Expressiveness of the Taxonomy	83
	4.3 A Use-Centered Development of a Taxonomy of Web Genres	85
	4.3.1 Research Design: Naturalistic Field Study	85
	4.3.2 Research Informants	85
	4.3.3 Data Elicitation	86
	4.3.4 Data Analysis	87
	4.4 Results	88
	4.5 Discussion	89
	4.6 Conclusions	92
	References	93
	Part III Automatic Web Genre Identification	95
	5 Cross-Testing a Genre Classification Model for the Web	96
	5.1 Introduction	96
	5.2 Approximating Genre Population on the Web	99
	5.2.1 Noise	100
	5.2.2 Description of the Corpora Used for Cross-Testing	101
	5.3 The Web as Communication	105
	5.3.1 Genre Palette	105
	5.3.2 Linguistically- and Functionally-Motivated Features	107
	5.4 The Genre Model	107
	5.4.1 Methodology	110
	5.4.2 Flow and Hypotheses	111
	5.5 Results	113
	5.5.1 Cross-Testing Performance on Single Labels: BBC and 7-Webgenre Collections	114
	5.5.2 Performances of Other Single-Label Models on the 7-Webgenre Collection	117
	5.5.3 Cross-Testing Performance on Single Labels: Mapped Web Genres	120
	5.5.4 Cross-Testing Performance on Single Labels: HCG and MCG in Isolation	122
	5.5.5 The SPIRIT Sample: An Attempt to Assess Multilabelling	122
	5.6 Discussion	126
	5.7 Conclusion and Future Work	127
	References	135
	6 Formulating Representative Features with Respect to Genre Classification	138
	6.1 Introduction	138
	6.2 Defining Genre Classification	141
	6.2.1 Document Representation in Conventional Text Classification	141
	6.2.2 Harmonic Descriptor Representation (HDR) of Documents	141
	6.2.3 Defining Genre	145
	6.3 Classifiers	146
	6.4 Dataset	147
	6.5 Features	149
	6.6 Results	151
	6.6.1 Overall Accuracy	151
	6.6.2 Precision and Recall	152
	6.7 Conclusions	154
	References	155
	7 In the Garden and in the Jungle	157
	7.1 Introduction	157
	7.2 Text Typology for the Web	159
	7.3 An Experiment in Automatic Classification of the Web	163
	7.4 Analysis of Results	167
	7.4.1 Qualitative Assessment of Texts in Each Category	167
	7.4.2 Assessing the Composition of ukWac	169
	7.5 Conclusions and Future Research	170
	References	173
	8 Web Genre Analysis: Use Cases, Retrieval Models, and Implementation Issues	175
	8.1 Introduction	175
	8.1.1 Contributions	176
	8.2 Use Cases: Genre Analysis in the Retrieval Practice	176
	8.2.1 Genre-Enabled Web Search	177
	8.2.2 Information Extraction Based on Genre Information	177
	8.2.3 Organizing Collections in Both Topic and Genre Dimensions<