Hidden Markov models (HMMs) are a powerful probabilistic tool for modeling sequential data, and have been applied with success to many text-related tasks, such as part-of-speech tagging, text segmentation and information extraction. In these cases, the observations are usually modeled as multinomial distributions over a discrete vocabulary, and the HMM parameters are set to maximize the likelihood of the observations. This paper presents a new Markovian sequence model, closely related to HMMs, that allows observations to be represented as arbitrary overlapping features (such as word, capitalization, formatting, part-of-speech), and defines the conditional probability of state sequences given observation sequences. It does this by using the maximum entropy framework to fit a set of exponential models that represent the probability of a state given an observation and the previous state. We present positive experimental results on the segmentation of FAQ’s. 1.

The CMU Statistical Language Modeling toolkit was released in 1994 in order to facilitate the construction and testing of bigram and trigram language models. It is currently in use in over 40 academic, government and industrial laboratories in over 12 countries. This paper presents a new version of the toolkit. We outline the conventional language modeling technology, as implemented in the toolkit, and describe the extra efficiency and functionality that the new toolkit provides as compared to previous software for this task. Finally,we give an example of the use of the toolkit in constructing and testing a simple language model.

This paper proposes the use of maximum entropy techniques for text classification. Maximum entropy is a probability distribution estimation technique widely used for a variety of natural language tasks, such as language modeling, part-of-speech tagging, and text segmentation. The underlying principle of maximum entropy is that without external knowledge, one should prefer distributions that are uniform. Constraints on the distribution, derived from labeled training data, inform the technique where to be minimally non-uniform. The maximum entropy formulation has a unique solution which can be found by the improved iterative scaling algorithm. In this paper, maximum entropy is used for text classification by estimating the conditional distribution of the class variable given the document. In experiments on several text datasets we compare accuracy to naive Bayes and show that maximum entropy is sometimes significantly better, but also sometimes worse. Much future work remains, but the re...

An adaptive statistical languagemodel is described, which successfullyintegrates long distancelinguistic information with other knowledge sources. Most existing statistical language models exploit only the immediate history of a text. To extract information from further back in the document's history, we propose and use trigger pairs as the basic information bearing elements. This allows the model to adapt its expectations to the topic of discourse. Next, statistical evidence from multiple sources must be combined. Traditionally, linear interpolation and its variants have been used, but these are shown here to be seriously deficient. Instead, we apply the principle of Maximum Entropy (ME). Each information source gives rise to a set of constraints, to be imposed on the combined estimate. The intersection of these constraints is the set of probability functions which are consistent with all the information sources. The function with the highest entropy within that set is the ME solution...

The World-Wide Web is developing very fast. Currently, finding useful information on the Web is a time consuming process. In this paper, we present WebMate, an agent that helps users to effectively browse and search the Web. WebMate extends the state of the art in Web-based information retrieval in many ways. First, it uses multiple TF-IDF vectors to keep track of user interests in different domains. These domains are automatically learned by WebMate. Second, WebMate uses the Trigger Pair Model to automatically extract keywords for refining document search. Third, during search, the user can provide multiple pages as similarity/relevance guidance for the search. The system extracts and combines relevant keywords from these relevant pages and uses them for keyword refinement. Using these techniques, WebMate provides effective browsing and searching help and also compiles and sends to users personal newspaper by automatically spiding news sources. We have experimentally evaluated the per...

iii Acknowledgments This work would not have been possible without the support of many people inside and outside of New York University. My advisor, Professor Ralph Grishman, has provided me with a great deal of useful advice, including suggesting the problem of named entity recognition to me as a promising application for maximum entropy modeling. More than that, he has helped me work through a great deal of literature in statistical computational linguistics and he generously supplied me with the necessary time, equipment, and resources of his research staff which enabled me to put together the MENE system. I would also like to thank the other members of NYU's Proteus project for their assistance. In particular, John Sterling helped me to develop the idea of integrating the Proteus parser with the MENE system in the month before the MUC-7 evaluation. He and Eugene Agichtein put in extremely long hours leading up to the evaluation and helped to make it a success. The work on porting the MENE system to Japanese would not have been possible without the assistance of my friend and colleague, Satoshi Sekine. In addition, I would like to thank him for helping me out as the only English-speaking participant in the IREX evaluation. For his assistance with my upcoming trip to Japan and for all his work on translating IREX instructions for my benefit, I am very grateful.

This paper describes a novel statistical namedentity (i.e. "proper name") recognition system built around a maximum enti W framework. By working within the framework of maximum entropy. theory and utilizing a flexible object-based architecture, the system is able to make use of an extraordinarily diverse range of knowledge sources in making its tagging decisions. These knowledge sources include capitalization features, lexical features, features in- dicating the current section of text (i.e. headline or main body), and dictionaries of single or multi-wtrd terms. The purely statistical system contains no hand-generated patterns and achieves a result comparable with the best statistical systems. However, when combined with other handcoded systems, the system achieves scores that exceed the highest comparable scores thus-far published.

Conditional random fields (Lafferty et al., 2001) are quite effective at sequence labeling tasks like shallow parsing (Sha and Pereira, 2003) and namedentity extraction (McCallum and Li, 2003). CRFs are log-linear, allowing the incorporation of arbitrary features into the model. To train on unlabeled data, we require unsupervised estimation methods for log-linear models; few exist. We describe a novel approach, contrastive estimation. We show that the new technique can be intuitively understood as exploiting implicit negative evidence and is computationally efficient. Applied to a sequence labeling problem—POS tagging given a tagging dictionary and unlabeled text—contrastive estimation outperforms EM (with the same feature set), is more robust to degradations of the dictionary, and can largely recover by modeling additional features. 1

Language modeling is the art of determining the probability of a sequence of words. This is useful in a large variety of areas including speech recognition, optical character recognition, handwriting recognition, machine translation, and spelling correction (Church, 1988; Brown et al., 1990; Hull, 1992; Kernighan et al., 1990; Srihari and Baltus, 1992). The most commonly used language models are very simple (e.g. a Katz-smoothed trigram model). There are many improvements over this simple model however, including caching, clustering, higherorder n-grams, skipping models, and sentence-mixture models, all of which we will describe below. Unfortunately, these more complicated techniques have rarely been examined in combination. It is entirely possible that two techniques that work well separately will not work well together, and, as we will show, even possible that some techniques will work better together than either one does by itself. In this...

Building models of language is a central task in natural language processing. Traditionally, language has been modeled with manually-constructed grammars that describe which strings are grammatical and which are not; however, with the recent availability of massive amounts of on-line text, statistically-trained models are an attractive alternative. These models are generally probabilistic, yielding a score reflecting sentence frequency instead of a binary grammaticality judgement. Probabilistic models of language are a fundamental tool in speech recognition for resolving acoustically ambiguous utterances. For example, we prefer the transcription forbear to four bear as the former string is far more frequent in English text. Probabilistic models also have application in optical character recognition, handwriting recognition, spelling correction, part-of-speech tagging, and machine translation. In this thesis, we investigate three problems involving the probabilistic modeling of languag...