TEMPEST: A Template Editor for Structured Text

TEMPEST is a full-screen text editor that incorporates a structural paradigm in addition to the more traditional textual paradigm provided by most editors. While the textual paradigm treats the text as a sequence of characters, the structural paradigm treats it as a collection of named blocks which the user can define, group, and manipulate. Blocks can be defined to correspond to the structural features of he text, thereby providing more meaningful objects to operate on than characters of lines. The structural representation of the text is kept in the background, giving TEMPEST the appearance of a typical text editor. The structural and textual interfaces coexist equally, however, so one can always operate on the text from wither point of view. TEMPEST's representation scheme provides no semantic understanding of structure. This approach sacrifices depth, but affords a broad range of applicability and requires very little computational overhead. A prototype has been implemented to illustrate the feasibility and potential areas of application of the central ideas. It was developed and runs on an IBM Personal Computer.

Improving Multi-class Text Classification with Naive Bayes

There are numerous text documents available in electronic form. More and more are becoming available every day. Such documents represent a massive amount of information that is easily accessible. Seeking value in this huge collection requires organization; much of the work of organizing documents can be automated through text classification. The accuracy and our understanding of such systems greatly influences their usefulness. In this paper, we seek 1) to advance the understanding of commonly used text classification techniques, and 2) through that understanding, improve the tools that are available for text classification. We begin by clarifying the assumptions made in the derivation of Naive Bayes, noting basic properties and proposing ways for its extension and improvement. Next, we investigate the quality of Naive Bayes parameter estimates and their impact on classification. Our analysis leads to a theorem which gives an explanation for the improvements that can be found in multiclass classification with Naive Bayes using Error-Correcting Output Codes. We use experimental evidence on two commonly-used data sets to exhibit an application of the theorem. Finally, we show fundamental flaws in a commonly-used feature selection algorithm and develop a statistics-based framework for text feature selection. Greater understanding of Naive Bayes and the properties of text allows us to make better use of it in text classification.

Improving Multiclass Text Classification with the Support Vector Machine

We compare Naive Bayes and Support Vector Machines on the task of multiclass text classification. Using a variety of approaches to combine the underlying binary classifiers, we find that SVMs substantially outperform Naive Bayes. We present full multiclass results on two well-known text data sets, including the lowest error to date on both data sets. We develop a new indicator of binary performance to show that the SVM's lower multiclass error is a result of its improved binary performance. Furthermore, we demonstrate and explore the surprising result that one-vs-all classification performs favorably compared to other approaches even though it has no error-correcting properties.