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

A literature survey of methods for analysis of subjective language

Subjective language is used to express attitudes and opinions towards things, ideas and people. While content and topic centred natural language processing is now part of everyday life, analysis of subjective aspects of natural language have until recently been largely neglected by the research community. The explosive growth of personal blogs, consumer opinion sites and social network applications in the last years, have however created increased interest in subjective language analysis. This paper provides an overview of recent research conducted in the area

Multiple Instance Learning: A Survey of Problem Characteristics and Applications

Multiple instance learning (MIL) is a form of weakly supervised learning where training instances are arranged in sets, called bags, and a label is provided for the entire bag. This formulation is gaining interest because it naturally fits various problems and allows to leverage weakly labeled data. Consequently, it has been used in diverse application fields such as computer vision and document classification. However, learning from bags raises important challenges that are unique to MIL. This paper provides a comprehensive survey of the characteristics which define and differentiate the types of MIL problems. Until now, these problem characteristics have not been formally identified and described. As a result, the variations in performance of MIL algorithms from one data set to another are difficult to explain. In this paper, MIL problem characteristics are grouped into four broad categories: the composition of the bags, the types of data distribution, the ambiguity of instance labels, and the task to be performed. Methods specialized to address each category are reviewed. Then, the extent to which these characteristics manifest themselves in key MIL application areas are described. Finally, experiments are conducted to compare the performance of 16 state-of-the-art MIL methods on selected problem characteristics. This paper provides insight on how the problem characteristics affect MIL algorithms, recommendations for future benchmarking and promising avenues for research