Topic Detection and Tracking (TDT) is a DARPA-sponsored initiative to investigate the state of the art in finding and following new events in a stream of broadcast news stories. The TDT problem consists of three major tasks: (1) segmenting a stream of data, especially recognized speech, into distinct stories; (2) identifying those news stories that are the first to discuss a new event occurring in the news; and (3) given a small number of sample news stories about an event, finding all following stories in the stream. The Pilot Study ran from September 1996 through October 1997. The primary participants were DARPA, Carnegie Mellon University, Dragon Systems, and the University of Massachusetts at Amherst. This report summarizes the findings of the pilot study. The TDT work continues in a new project involving larger training and test corpora, more active participants, and a more broadly defined notion of "topic" than was used in the pilot study.

In certain contexts, maximum entropy (ME) modeling can be viewed as maximum likelihood training for exponential models, and like other maximum likelihood methods is prone to overfitting of training data. Several smoothing methods for maximum entropy models have been proposed to address this problem, but previous results do not make it clear how these smoothing methods compare with smoothing methods for other types of related models. In this work, we survey previous work in maximum entropy smoothing and compare the performance of several of these algorithms with conventional techniques for smoothing n-gram language models. Because of the mature body of research in n-gram model smoothing and the close connection between maximum entropy and conventional n-gram models, this domain is well-suited to gauge the performance of maximum entropy smoothing methods. Over a large number of data sets, we find that an ME smoothing method proposed to us by Lafferty [1] performs as well as or better tha...

Statistical Language Models estimate the distribution of various natural language phenomena for the purpose of speech recognition and other language technologies. Since the first significant model was proposed in 1980, many attempts have been made to improve the state of the art. We review them here, point to a few promising directions, and argue for a Bayesian approach to integration of linguistic theories with data. 1. OUTLINE Statistical language modeling (SLM) is the attempt to capture regularities of natural language for the purpose of improving the performance of various natural language applications. By and large, statistical language modeling amounts to estimating the probability distribution of various linguistic units, such as words, sentences, and whole documents. Statistical language modeling is crucial for a large variety of language technology applications. These include speech recognition (where SLM got its start), machine translation, document classification and routing, optical character recognition, information retrieval, handwriting recognition, spelling correction, and many more. In machine translation, for example, purely statistical approaches have been introduced in [1]. But even researchers using rule-based approaches have found it beneficial to introduce some elements of SLM and statistical estimation [2]. In information retrieval, a language modeling approach was recently proposed by [3], and a statistical/information theoretical approach was developed by [4]. SLM employs statistical estimation techniques using language training data, that is, text. Because of the categorical nature of language, and the large vocabularies people naturally use, statistical techniques must estimate a large number of parameters, and consequently depend critically on the availability of large amounts of training data.

This paper describes a method for linear text seg- mc. ntation which is twice as accurate and over seven times as fast as the state-of-the-art (Reynar, 1998). Inter-sentence similarity is replaced by rank in the local context. Boundary locations are discovered by divisive clustering.

Abstract—In certain contexts, maximum entropy (ME) modeling can be viewed as maximum likelihood (ML) training for exponential models, and like other ML methods is prone to overfitting of training data. Several smoothing methods for ME models have been proposed to address this problem, but previous results do not make it clear how these smoothing methods compare with smoothing methods for other types of related models. In this work, we survey previous work in ME smoothing and compare the performance of several of these algorithms with conventional techniques for smoothing-gram language models. Because of the mature body of research in-gram model smoothing and the close connection between ME and conventional-gram models, this domain is well-suited to gauge the performance of ME smoothing methods. Over a large number of data sets, we find that fuzzy ME smoothing performs as well as or better than all other algorithms under consideration. We contrast this method with previous-gram smoothing methods to explain its superior performance. Index Terms—Exponential models, language modeling, maximum entropy, minimum divergence,-gram models, smoothing.

Abstract not found

This paper introduces a new statistical approach to partitioning text automatically into coherent segments. Our approach enlists both short-range and long-range language models to help it sniff out likely sites of topic changes in text. To aid its search, the system consults a set of simple lexical hints it has learned to associate with the presence of boundaries through inspection of a large corpus of annotated data. We also propose a new probabilistically motivated ' error metric for use by the natural language processing and information retrieval communities, intended to supersede precision and recall for appraising segmentation algorithms. Qualitative assessment of our algorithm as well as evaluation using this new metric demonstrate the effective- ness of our approach in two very different domains, Wall Street Journal articles and the TDT Corpus, a collection of newswire articles and broadcast news transcripts.

The most widely-used evaluation metric for language models for speech recognition is the perplexity of test data. While perplexities can be calculated efficiently and without access to a speech recognizer, they often do not correlate well with speech recognition word-error rates. In this research, we attempt to find a measure that like perplexity is easily calculated but which better predicts speech recognition performance. We investigate two approaches; first, we attempt to extend perplexity by using similar measures that utilize information about language models that perplexity ignores. Second, we attempt to imitate the word-error calculation without using a speech recognizer by artificially generating speech recognition lattices. To test our new metrics, we have built over thirty varied language models. We find that perplexity correlates with word-error rate remarkably well when only considering n-gram models trained on in-domain data. When considering other types of models, our novel metrics are superior to perplexity for predicting speech recognition performance. However, we conclude that none of these measures predict word-error rate sufficiently accurately to be effective tools for language model evaluation in speech recognition.

This work has been motivated by two long term goals: to understand how humans learn language and to build programs that can understand language. Using a representation that makes the relevant features explicit is a prerequisite for successful learning and understanding. Therefore, I chose to represent relations between individual words explicitly in my model. Lexical attraction is defined as the likelihood of such relations. I introduce a new class of probabilistic language models named lexical attraction models which can represent long distance relations between words and I formalize this new class of models using information theory. Within the

This paper describes a lightweight method for the automatic insertion of intra-sentence punctuation into text. Despite the intuition that pauses in an acoustic stream are a positive indicator for some types of punctuation, this work will demonstrate the feasibility of a system which relies solely on lexical information. Besides its potential role in a speech recognition system, such a system could serve equally well in non-speech applications such as automatic grammar correction in a word processor and parsing of spoken text. After describing the design of a punctuationrestoration system, which relies on a trigram language model and a straightforward application of the Viterbi algorithm, we summarize results, both quantitative and subjective, of the performance and behavior of a prototype system. 1. INTRODUCTION The requirement that conventional speech dictation systems impose on the user to enunciate punctuation can often be an annoyance and in some situations even an impossibility. ...