Search results for: Fred J. Damerau

Items from 1 to 10 out of 10 results

chapter

Finding Structure in a Document Collection

Sholom M. Weiss, Nitin Indurkhya, Tong Zhang, Fred J. Damerau

Prediction methods look at stored examples with correct answers and project answers for new examples. One would expect that if we cannot obtain answers for the training examples, then the process cannot be completed. Given a collection of documents, we have no problem transforming the unstructured set of words for each document into a structured spreadsheet. But the last column also must be filled...

chapter

Case Studies

Sholom M. Weiss, Nitin Indurkhya, Tong Zhang, Fred J. Damerau

Text Mining > 157-195

Our approach to text mining is motivated by practical applications. However, the design and development of prediction methods often take place in a controlled scientific environment that simulates the real world. This is necessary for comparative analyses and also for unraveling the pieces of the puzzle that constitute a prediction problem. The question remains as to the appropriateness of these methods...

chapter

Using Text for Prediction

Sholom M. Weiss, Nitin Indurkhya, Tong Zhang, Fred J. Damerau

Text Mining > 47-84

The words prediction and forecast conjure up images of momentous decisions and complex processes fraught with inaccuracies. From a statistical perspective, it’s a straightforward problem that has a solution. Of course, the solution may not always be very good. The problem presents itself as in Figure 3.1. Given a sample of examples of past experience, we project to new examples. If the future is similar...

chapter

Emerging Directions

Sholom M. Weiss, Nitin Indurkhya, Tong Zhang, Fred J. Damerau

Text Mining > 197-211

Our principal objective is predictive text mining. The wealth of research literature for text mining encompasses a much wider range of topics than is presented here. At the same time, the research literature deals with each of these topics in great depth, describing many alternatives to the approaches that we have selected. Our description is not a comprehensive review of the field. We have used our...

chapter

Looking for Information in Documents

Sholom M. Weiss, Nitin Indurkhya, Tong Zhang, Fred J. Damerau

Text Mining > 129-156

An important research area for natural language processing and text mining is the extraction and formatting of information from unstructured text. One can think of the end goal of information extraction in terms of filling templates codifying the extracted information. In this chapter, we shall describe information extraction from this perspective and some machine-learning methods that can be used...

chapter

Information Retrieval and Text Mining

Sholom M. Weiss, Nitin Indurkhya, Tong Zhang, Fred J. Damerau

Text Mining > 85-102

What is the principal computer specialty for processing documents and text? Many experts would respond “Information Retrieval.” The task of information retrieval, or IR as its practitioners call it, is to retrieve relevant documents in response to a query. Figure 4.1 illustrates the objectives of information retrieval of documents, where (a) a general description is given of the query, (b) the document...

chapter

Overview of Text Mining

Sholom M. Weiss, Nitin Indurkhya, Tong Zhang, Fred J. Damerau

Text Mining > 1-13

Do you have a shortage of data? Not very likely. A consequence of the pervasive use of computers is that most data originate in digital form. If we trade a stock or write a book or buy a product online, these events evolve electronically. Since so many paper transactions are now in paperless digital form, lots of “big” data are available for further analysis.

chapter

From Textual Information to Numerical Vectors

Sholom M. Weiss, Nitin Indurkhya, Tong Zhang, Fred J. Damerau

Text Mining > 15-46

To mine text, we first need to process it into a form that data-mining procedures can use. As mentioned in the previous chapter, this typically involves generating features in a spreadsheet format. Classical data mining looks at highly structured data. Our spreadsheet model is the embodiment of a representation that is supportive of predictive modeling. In some ways, predictive text mining is simpler...

book

Text Mining

Sholom M. Weiss, Nitin Indurkhya, Tong Zhang, Fred J. Damerau, more

One consequence of the pervasive use of computers is that most documents originate in digital form. Text mining—the process of searching, retrieving, and analyzing unstructured, natural-language text—is concerned with how to exploit the textual data embedded in these documents. Text Mining presents a comprehensive introduction and overview of the field, integrating related topics (such as artificial...

article

Text categorization for a comprehensive time-dependent benchmark

Fred J. Damerau, Tong Zhang, Sholom M. Weiss, Nitin Indurkhya

Information Processing and Management > 2004 > 40 > 2 > 209-221

We present results for automated text categorization of the Reuters-810000 collection of news stories. Our experiments use the entire one-year collection of 810,000 stories and the entire subject index. We divide the data into monthly groups and provide an initial benchmark of text categorization performance on the complete collection. Experimental results show that efficient sparse-feature implementations...

Filter options

Publication date

Set your own date range

INFONA - science communication portal

Search results for: Fred J. Damerau

Add recipient

Sending message cancelled

Are you sure you want to cancel sending this message?

Send message

Filter options

Publication date

Date range setting

Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.

Content availability

Publication type

Keywords

Data set

Reporting an error / abuse

Sending the report failed

Accessibility options