Text streams are ubiquitous and contain a wealth of information, but are typically orders of magnitude too large in scale for comprehensive human inspection. There is a need for tools that can detect and group changes occurring within text streams and substreams, in order to find, structure, and summarize these changes for presentation to human analysts. This paper describes a procedure for efficiently finding step changes, trends, bursts, and cyclic changes affecting frequencies of words, or more general lexical items, within streams of documents which may be optionally labeled with metadata. The common phenomenon of over-dispersion is accommodated using mixture distributions. A streaming implementation is described which can process data from a continuous feed. Anomalies can be detected, grouped, and rendered visually for human comprehension.

Coping with Information Overload is a major challenge of the 21stcentury. Huge volumes and varieties of multilingual data must be processed to extract salient information. Previous research has addressed automatic characterization of streaming content. However, information includes both content and associated meta-data, which humans deal with as a gestalt but computer systems often treat separately. Random attributed graphs provide an effective means to characterize and draw inferences from large volumes of language content plus associated meta-data. This paper describes these methods and their utility, with experimental proof-of-concept on the Switchboard and Enron corpora.

The goal of this research is to infer traits about groups of people from their turn-taking behavior in natural conversation. These traits are latent attributes in a social network, whose relative frequencies we estimate from content-derived metadata. Our approach is to train statistical models of turn-taking behavior using automatic labels of speech activity, and measure the association of these models with socially correlated traits. We experimentally evaluate these ideas using the Switchboard-1 speech corpus, which provides speech content and metadata associated with each speaker, such as gender, age and education, as well as inferred social correlates such as willingness to participate and initiate. We show that population proportions of these socially correlated externals can be predicted with a root mean-squared error of approximately 0.1 across all mixture proportions.

In processing large volumes of speech and language data, we are often interested in the distribution of languages, speakers, topics, etc. For large data sets, these distributions are typically estimated at a given point in time using pattern classification technology. Such estimates can be highly biased, especially for rare classes. While these biases have been addressed in some applications, they have thus far been ignored in the speech and language literature. This neglect causes significant error for low-frequency classes. Correcting this biased distribution involves exploiting uncertain knowledge of the classifier error patterns. The Metropolis-Hastings algorithm allows us to construct a Bayes estimator for the true class proportions. We experimentally evaluate this algorithm for a speaker recognition task. In this experiment, the Bayes estimator reduces maximum RMSE by a factor of five. Performance is furthermore more consistent, with range of RMSE reduced by a factor of 4.

In processing large volumes of speech and language data, we are often interested in the distribution of languages, speakers, topics, etc. For large data sets, these distributions are typically estimated at a given point in time using pattern classification technology. It is well known that such estimates can be highly biased, especially for rare classes. While these biases have been addressed in some applications, they have thus far been ignored in the speech and language literature. This neglect causes significant error for low-frequency classes. Correcting this biased distribution involves exploiting uncertain knowledge of the classifier error patterns. We describe a numerical method, the Metropolis-Hastings (M-H) algorithm, which provides a Bayes estimator for the distribution. We experimentally evaluate this algorithm for a speaker recognition task, demonstrating a fivefold reduction in root mean squared error.

One problem that has arisen in recent years is the extraction of useful information from changes in a data stream including natural language. Statistical tests on single word occurrences can reveal many apparent differences. Understanding the reasons behind such changes in the data requires methods for discovering structure within the entire set of individual changed items. This work presents a methodology for understanding how a language model has altered based on utterance clustering and statistical tests on individual features. It further examines clustering of lexical items via profiles of changes in association scores. A machine using an analysis package based on these techniques can isolate novel portions of the data stream. Human inspection of such data then readily determines the nature of the observed change. We investigate several variants of this analysis upon data drawn from an automated call center.