Automatic Classification of Web Search Results: Product Review vs. Non-review Documents

Abstract. This study seeks to develop an automatic method to identify product review documents on the Web using the snippets (summary information that includes the URL, title, and summary text) returned by the Web search engine. The aim is to allow the user to extend topical search with genre-based filtering or categorization. Firstly we applied a common machine learning technique, SVM (Support Vector Machine), to investigate which features of the snippets are useful for classification. The best results were obtained using just the title and URL (domain and folder names) of the snippets as phrase terms (n-grams). Then we developed a heuristic approach that utilizes domain knowledge constructed semi-automatically, and found that it performs comparatively well, with only a small drop in accuracy rates. A hybrid approach which combines both the machine learning and heuristic approaches performs slightly better than the machine learning approach alone

Structured text retrieval by means of affordances and genre.

This paper offers a proposal for some preliminary research on the retrieval of structured text, such as extensible mark-up language (XML). We believe that capturing the way in which a reader perceives the meaning of documents, especially genres of text, may have implications for information retrieval (IR) and in particular, for cognitive IR and relevance. Previous research on shallow features of structured text has shown that categorization by form is possible. Gibsons theory of affordances and genre offer the reader the meaning and purpose - through structure - of a text, before the reader has even begun to read it, and should therefore provide a good basis for the deep skimming and categorization of texts. We believe that Gibsons affordances will aid the user to locate, examine and utilize shallow or deep features of genres and retrieve relevant output. Our proposal puts forward two hypotheses, with a list of research questions to test them, and culminates in experiments involving the studies of human categorization behaviour when viewing the structures of emails and web documents. Finally, we will examine the effectiveness of adding structural layout cues to a Yahoo discussion forum (currently only a bag-of-words), which is rich in structure, but only searchable through a Boolean search engine

Retrieval Models for Genre Classification

Genre provides a characterization of a document with respect to its form or functional trait. Genre is orthogonal to topic, rendering genre information a powerful filter technology for information seekers in digital libraries. However, an efficient means for genre classification is an open and controversially discussed issue. This paper gives an overview and presents new results related to automatic genre classification of text documents. We present a comprehensive survey which contrasts the genre retrieval models that have been developed for Web and non-Web corpora. With the concept of genre-specific core vocabularies the paper provides an original contribution related to computational aspects and classification performance of genre retrieval models: we show how such vocabularies are acquired automatically and introduce new concentration measures that quantify the vocabulary distribution in a sensible way. Based on these findings we construct lightweight genre retrieval models and evaluate their discriminative power and computational efficiency. The presented concepts go beyond the existing utilization of vocabulary-centered, genre-revealing features and open new possibilities for the construction of genre classifiers that operate in real-time

Intelligent Information Interaction for Managing Distributed Collections of Web Documents

Digital collections are ubiquitous. However, not all digital collections are the same. While most digital collections have limited forms of change - primarily creation and deletion of additional resources - there exists a class of digital collections that undergo additional kinds of change. These collections are made up of resources that are distributed across the Internet and brought together into the collection via hyperlinking. This means the underlying collection members are not controlled by the curator of the collection. Resources can be expected to change as time goes on. To further complicate matters these collections can be hard to maintain when they are large, highly dynamic, or lacking active curation. Part of the difficulty in maintaining these collections is determining if a changed page is still a valid member of the collection. While others have tried to address this problem by measuring change and defining a maximum allowed threshold of change, these methods treat all change as a potential problems and treat web content as a static document despite its intrinsically dynamic nature. Instead, I approach the problem of determining significance of change on the web by embracing it as a normal part of a web document's lifecycle, Instead of using thresholds to identify abnormal changes, I determine the difference between what a maintainer expects a page to do and what it actually does. These models are created using a variety of feature extractors to find pertinent information in a page, a Kalman filter to model the history of a page and predict a next version and finally classification of results into either expected or unexpected change. I evaluate the different options for extractors and analyzers to determine the best options from my suite of possibilities. This work is informed by a series of studies on both web pages and potential collection maintainers, observations of the NSDL Pathways, and a ground-truth set of blog changes tagged by a human judgment of the kind of change. The results of this work showed a statistically significant improvement over a range of traditional threshold techniques when applied to the collection of tagged blog changes

What is the influence of genre during the perception of structured text for retrieval and search?

This thesis presents an investigation into the high value of structured text (or form) in the context of genre within Information Retrieval. In particular, how are these structured texts perceived and why are they not more heavily used within Information Retrieval & Search communities? The main motivation is to show the features in which people can exploit genre within Information Search & Retrieval, in particular, categorisation and search tasks. To do this, it was vital to record and analyse how and why this was done during typical tasks. The literature review highlighted two previous studies (Toms & Campbell 1999a; Watt 2009) which have reported pilot studies consisting of genre categorisation and information searching. Both studies and other findings within the literature review inspired the work contained within this thesis. Genre is notoriously hard to define, but a very useful framework of Purpose and Form, developed by Yates & Orlikowski (1992), was utilised to design two user studies for the research reported within the thesis. The two studies consisted of, first, a categorisation task (e-mails), and second, a set of six simulated situations in Wikipedia, both of which collected quantitative data from eye tracking experiments as well as qualitative user data. The results of both studies showed the extent to which the participants utilised the form features of the stimuli presented, in particular, how these were used, which ocular behaviours (skimming or scanning) and actual features were used, and which were the most important. The main contributions to research made by this thesis were, first of all, that the task-based user evaluations employing simulated search scenarios revealed how and why users make decisions while interacting with the textual features of structure and layout within a discourse community, and, secondly, an extensive evaluation of the quantitative data revealed the features that were used by the participants in the user studies and the effects of the interpretation of genre in the search and categorisation process as well as the perceptual processes used in the various communities. This will be of benefit for the re-development of information systems. As far as is known, this is the first detailed and systematic investigation into the types of features, value of form, perception of features, and layout of genre using eye tracking in online communities, such as Wikipedia