Results 11 - 20
of
662
Using Maximum Entropy for Text Classification
, 1999
"... 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 principl ..."
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Cited by 326 (6 self)
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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...
Improving Text Classification by Shrinkage in a Hierarchy of Classes
, 1998
"... When documents are organized in a large number of topic categories, the categories are often arranged in a hierarchy. The U.S. patent database and Yahoo are two examples. ..."
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Cited by 289 (6 self)
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When documents are organized in a large number of topic categories, the categories are often arranged in a hierarchy. The U.S. patent database and Yahoo are two examples.
Less is more: Active learning with support vector machines
, 2000
"... We describe a simple active learning heuristic which greatly enhances the generalization behavior of support vector machines (SVMs) on several practical document classification tasks. We observe a number of benefits, the most surprising of which is that a SVM trained on a wellchosen subset of the av ..."
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Cited by 278 (1 self)
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We describe a simple active learning heuristic which greatly enhances the generalization behavior of support vector machines (SVMs) on several practical document classification tasks. We observe a number of benefits, the most surprising of which is that a SVM trained on a wellchosen subset of the available corpus frequently performs better than one trained on all available data. The heuristic for choosing this subset is simple to compute, and makes no use of information about the test set. Given that the training time of SVMs depends heavily on the training set size, our heuristic not only offers better performance with fewer data, it frequently does so in less time than the naive approach of training on all available data. 1.
Impact of similarity measures on web-page clustering,”
- in Workshop on Artificial Intelligence for Web Search (AAAI
, 2000
"... Abstract Clustering of web documents enables (semi-)automated categorization, and facilitates certain types of search. Any clustering method has to embed the documents in a suitable similarity space. While several clustering methods and the associated similarity measures have been proposed in the p ..."
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Cited by 205 (26 self)
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Abstract Clustering of web documents enables (semi-)automated categorization, and facilitates certain types of search. Any clustering method has to embed the documents in a suitable similarity space. While several clustering methods and the associated similarity measures have been proposed in the past, there is no systematic comparative study of the impact of similarity metrics on cluster quality, possibly because the popular cost criteria do not readily translate across qualitatively different metrics. We observe that in domains such as YA-HOO that provide a categorization by human experts, a useful criteria for comparisons across similarity metrics is indeed available. We then compare four popular similarity measures (Euclidean, cosine, Pearson correlation and extended Jaccard) in conjunction with several clustering techniques (random, self-organizing feature map, hyper-graph partitioning, generalized kmeans, weighted graph partitioning), on high dimensionai sparse data representing web documents. Performance is measured against a human-imposed classification into news categories and industry categories. We conduct a number of experiments and use t-tests to assure statistical significance of results. Cosine and extended Jaccard similarities emerge as the best measures to capture human categorization behavior, while Euclidean performs poorest. Also, weighted graph partitioning approaches are clearly superior to all others.
Multi-label text classification with a mixture model trained by EM
- AAAI 99 Workshop on Text Learning
, 1999
"... In many important document classification tasks, documents may each be associated with multiple class labels. This paper describes a Bayesian classification approach in which the multiple classes that comprise a document are represented by a mixture model. While the labeled training data indicates w ..."
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Cited by 178 (4 self)
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In many important document classification tasks, documents may each be associated with multiple class labels. This paper describes a Bayesian classification approach in which the multiple classes that comprise a document are represented by a mixture model. While the labeled training data indicates which classes were responsible for generating a document, it does not indicate which class was responsible for generating each word. Thus we use EM to fill in this missing value, learning both the distribution over mixture weights and the word distribution in each class's mixture component. We describe the benefits of this model and present preliminary results with the Reuters-21578 data set.
Is All That Talk Just Noise ? The Information Content of Internet Stock Message Boards
- Journal of Finance
, 2004
"... Financial press reports claim that internet stock message boards can move markets. We study the effect of more than 1.5 million messages posted on Yahoo! Finance and Raging Bull about the 45 companies in the Dow Jones Industrial Average, and the Dow Jones Internet Index. The bullishness of the messa ..."
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Cited by 164 (2 self)
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Financial press reports claim that internet stock message boards can move markets. We study the effect of more than 1.5 million messages posted on Yahoo! Finance and Raging Bull about the 45 companies in the Dow Jones Industrial Average, and the Dow Jones Internet Index. The bullishness of the messages is measured using computational linguistics methods. News stories reported in the Wall Street Journal are used as controls. We find significant evidence that the stock messages help predict market volatility, but not stock returns. Consistent with Harris and Raviv (1993), agreement among the posted messages is associated with decreased trading volume. (JEL: G12, G14)
Content-based recommendation systems
- THE ADAPTIVE WEB: METHODS AND STRATEGIES OF WEB PERSONALIZATION. VOLUME 4321 OF LECTURE NOTES IN COMPUTER SCIENCE
, 2007
"... This chapter discusses content-based recommendation systems, i.e., systems that recommend an item to a user based upon a description of the item and a profile of the user’s interests. Content-based recommendation systems may be used in a variety of domains ranging from recommending web pages, news ..."
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Cited by 163 (0 self)
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This chapter discusses content-based recommendation systems, i.e., systems that recommend an item to a user based upon a description of the item and a profile of the user’s interests. Content-based recommendation systems may be used in a variety of domains ranging from recommending web pages, news articles, restaurants, television programs, and items for sale. Although the details of various systems differ, content-based recommendation systems share in common a means for describing the items that may be recommended, a means for creating a profile of the user that describes the types of items the user likes, and a means of comparing items to the user profile to determine what to recommend. The profile is often created and updated automatically in response to feedback on the desirability of items that have been presented to the user. A common scenario for modern recommendation systems is a Web application with which a user interacts. Typically, a system presents a summary list of items to a user, and the user selects among the items to receive more details on an item or to interact
Centroid-Based Document Classification: Analysis & Experimental Results
, 2000
"... In this paper we present a simple linear-time centroid-based document classification algorithm, that despite its simplicity and robust performance, has not been extensively studied and analyzed. Our experiments show that this centroid-based classifier consistently and substantially outperforms o ..."
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Cited by 138 (1 self)
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In this paper we present a simple linear-time centroid-based document classification algorithm, that despite its simplicity and robust performance, has not been extensively studied and analyzed. Our experiments show that this centroid-based classifier consistently and substantially outperforms other algorithms such as Naive Bayesian, k-nearest-neighbors, and C4.5, on a wide range of datasets. Our analysis shows that the similarity measure used by the centroidbased scheme allows it to classify a new document based on how closely its behavior matches the behavior of the documents belonging to different classes. This matching allows it to dynamically adjust for classes with different densities and accounts for dependencies between the terms in the different classes.
Hierarchical Text Classification and Evaluation
, 2001
"... Hierarchical Classification refers to assigning of one or more suitable categories from a hierarchical category space to a document. While previous work in hierarchical classification focused on virtual category trees where documents are assigned only to the leaf categories, we propose a topdown lev ..."
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Cited by 134 (2 self)
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Hierarchical Classification refers to assigning of one or more suitable categories from a hierarchical category space to a document. While previous work in hierarchical classification focused on virtual category trees where documents are assigned only to the leaf categories, we propose a topdown level-based classification method that can classify documents to both leaf and internal categories. As the standard performance measures assume independence between categories, they have not considered the documents incorrectly classified into categories that are similar or not far from the correct ones in the category tree. We therefore propose the Category-Similarity Measures and DistanceBased Measures to consider the degree of misclassification in measuring the classification performance. An experiment has been carried out to measure the performance of our proposed hierarchical classification method. The results showed that our method performs well for Reuters text collection when enough training documents are given and the new measures have indeed considered the contributions of misclassified documents.
Mining the Biomedical Literature in the Genomic Era: An Overview
- JOURNAL OF COMPUTATIONAL BIOLOGY
, 2003
"... The past decade has seen a tremendous growth in the amount of experimental and computational biomedical data, specifically in the areas of Genomics and Proteomics. This growth is accompanied by an accelerated increase in the number of biomedical publications discussing the findings. In the last f ..."
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Cited by 132 (5 self)
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The past decade has seen a tremendous growth in the amount of experimental and computational biomedical data, specifically in the areas of Genomics and Proteomics. This growth is accompanied by an accelerated increase in the number of biomedical publications discussing the findings. In the last few years there is a lot of interest within the scientific community in literature-mining tools to help sort through this abundance of literature, and find the nuggets of information most relevant and useful for specific analysis tasks. This paper
