TERM WEIGHTING BASED ON INDEX OF GENRE FOR WEB PAGE GENRE CLASSIFICATION

Automating the identification of the genre of web pages becomes an important area in web pages classification, as it can be used to improve the quality of the web search result and to reduce search time. To index the terms used in classification, generally the selected type of weighting is the document-based TF-IDF. However, this method does not consider genre, whereas web page documents have a type of categorization called genre. With the existence of genre, the term appearing often in a genre should be more significant in document indexing compared to the term appearing frequently in many genres despites its high TF-IDF value. We proposed a new weighting method for web page documents indexing called inverse genre frequency (IGF). This method is based on genre, a manual categorization done semantically from previous research. Experimental results show that the term weighting based on index of genre (TF-IGF) performed better compared to term weighting based on index of document (TF-IDF), with the highest value of accuracy, precision, recall, and F-measure in case of excluding the genre-specific keywords were 78%, 80.2%, 78%, and 77.4% respectively, and in case of including the genre-specific keywords were 78.9%, 78.7%, 78.9%, and 78.1% respectively

Training a Genre Classifier for Automatic Classification of Web Pages

This paper presents experiments on classifying web pages by genre. Firstly, a corpus of 1539 manually labeled web pages was prepared. Secondly, 502 genre features were selected based on the literature and the observation of the corpus. Thirdly, these features were extracted from the corpus to obtain a data set. Finally, two machine learning algorithms, one for induction of decision trees (J48) and one ensemble algorithm (bagging), were trained and tested on the data set. The ensemble algorithm achieved on average 17% better precision and 1.6% better accuracy, but slightly worse recall; F-measure did not vary significantly. The results indicate that classification by genre could be a useful addition to search engines

Human Annotation and Automatic Detection of Web Genres

Texts differ from each other in various dimensions such as topic, sentiment, authorship and genre. In this thesis, the dimension of text variation of interest is genre. Unlike topic classification, genre classification focuses on the functional purpose of documents and classifies them into categories such as news, review, online shop, personal home page and conversational forum. In other words, genre classification allows the identification of documents that are similar in terms of purpose, even they are topically very diverse. Research on web genres has been motivated by the idea that finding information on the web can be made easier and more effective by automatic classification techniques that differentiate among web documents with respect to their genres. Following this idea, during the past two decades, researchers have investigated the performance of various genre classification algorithms in order to enhance search engines. Therefore, current web automatic genre identification research has resulted in several genre annotated web-corpora as well as a variety of supervised machine learning algorithms on these corpora. However, previous research suffers from shortcomings in corpus collection and annotation (in particular, low human reliability in genre annotation), which then makes the supervised machine learning results hard to assess and compare to each other as no reliable benchmarks exist. This thesis addresses this shortcoming. First, we built the Leeds Web Genre Corpus Balanced-design (LWGC-B) which is the first reliably annotated corpus for web genres, using crowd-sourcing for genre annotation. This corpus which was compiled by focused search method, overcomes the drawbacks of previous genre annotation efforts such as low inter-coder agreement and false correlation between genre and topic classes. Second, we use this corpus as a benchmark to determine the best features for closed-set supervised machine learning of web genres. Third, we enhance the prevailing supervised machine learning paradigm by using semi-supervised graph-based approaches that make use of the graph-structure of the web to improve classification results. Forth, we expanded our annotation method successfully to Leeds Web Genre Corpus Random (LWGC-R) where the pages to be annotated are collected randomly by querying search engines. This randomly collected corpus also allowed us to investigate coverage of the underlying genre inventory. The result shows that our 15 genre categories are sufficient to cover the majority but not the vast majority of the random web pages. The unique property of the LWGC-R corpus (i.e. having web pages that do not belong to any of the predefined genre classes which we refer to as noise) allowed us to, for the first time, evaluate the performance of an open-set genre classification algorithm on a dataset with noise. The outcome of this experiment indicates that automatic open-set genre classification is a much more challenging task compared to closed-set genre classification due to noise. The results also show that automatic detection of some genre classes is more robust to noise compared to other genre classes

TERM WEIGHTING BASED ON INDEX OF GENRE FOR WEB PAGE GENRE CLASSIFICATION

Automating the identification of the genre of web pages becomes an important area in web pages classification, as it can be used to improve the quality of the web search result and to reduce search time. To index the terms used in classification, generally the selected type of weighting is the document-based TF-IDF. However, this method does not consider genre, whereas web page documents have a type of categorization called genre. With the existence of genre, the term appearing often in a genre should be more significant in document indexing compared to the term appearing frequently in many genres despites its high TF-IDF value. We proposed a new weighting method for web page documents indexing called inverse genre frequency (IGF). This method is based on genre, a manual categorization done semantically from previous research. Experimental results show that the term weighting based on index of genre (TF-IGF) performed better compared to term weighting based on index of document (TF-IDF), with the highest value of accuracy, precision, recall, and F-measure in case of excluding the genre-specific keywords were 78%, 80.2%, 78%, and 77.4% respectively, and in case of including the genre-specific keywords were 78.9%, 78.7%, 78.9%, and 78.1% respectively

Automatic Genre Classification in Web Pages Applied to Web Comments

Automatic Web comment detection could significantly facilitate information retrieval systems, e.g., a focused Web crawler. In this paper, we propose a text genre classifier for Web text segments as intermediate step for Web comment detection in Web pages. Different feature types and classifiers are analyzed for this purpose. We compare the two-level approach to state-of-the-art techniques operating on the whole Web page text and show that accuracy can be improved significantly. Finally, we illustrate the applicability for information retrieval systems by evaluating our approach on Web pages achieved by a Web crawler

Methodologies for the Automatic Location of Academic and Educational Texts on the Internet

Traditionally online databases of web resources have been compiled by a human editor, or though the submissions of authors or interested parties. Considerable resources are needed to maintain a constant level of input and relevance in the face of increasing material quantity and quality, and much of what is in databases is of an ephemeral nature. These pressures dictate that many databases stagnate after an initial period of enthusiastic data entry. The solution to this problem would seem to be the automatic harvesting of resources, however, this process necessitates the automatic classification of resources as ‘appropriate’ to a given database, a problem only solved by complex text content analysis. This paper outlines the component methodologies necessary to construct such an automated harvesting system, including a number of novel approaches. In particular this paper looks at the specific problems of automatically identifying academic research work and Higher Education pedagogic materials. Where appropriate, experimental data is presented from searches in the field of Geography as well as the Earth and Environmental Sciences. In addition, appropriate software is reviewed where it exists, and future directions are outlined

Methodologies for the Automatic Location of Academic and Educational Texts on the Internet

Traditionally online databases of web resources have been compiled by a human editor, or though the submissions of authors or interested parties. Considerable resources are needed to maintain a constant level of input and relevance in the face of increasing material quantity and quality, and much of what is in databases is of an ephemeral nature. These pressures dictate that many databases stagnate after an initial period of enthusiastic data entry. The solution to this problem would seem to be the automatic harvesting of resources, however, this process necessitates the automatic classification of resources as ‘appropriate’ to a given database, a problem only solved by complex text content analysis. This paper outlines the component methodologies necessary to construct such an automated harvesting system, including a number of novel approaches. In particular this paper looks at the specific problems of automatically identifying academic research work and Higher Education pedagogic materials. Where appropriate, experimental data is presented from searches in the field of Geography as well as the Earth and Environmental Sciences. In addition, appropriate software is reviewed where it exists, and future directions are outlined

Conventions and mutual expectations — understanding sources for web genres

Genres can be understood in many different ways. They are often perceived as a primarily sociological construction, or, alternatively, as a stylostatistically observable objective characteristic of texts. The latter view is more common in the research field of information and language technology. These two views can be quite compatible and can inform each other; this present investigation discusses knowledge sources for studying genre variation and change by observing reader and author behaviour rather than performing analyses on the information objects themselves