Results 1 - 10 of 4,286

A Maximum Entropy Model for Part-Of-Speech Tagging

by Adwait Ratnaparkhi , 1996
"... This paper presents a statistical model which trains from a corpus annotated with Part-OfSpeech tags and assigns them to previously unseen text with state-of-the-art accuracy(96.6%). The model can be classified as a Maximum Entropy model and simultaneously uses many contextual "features" t ..."
Abstract - Cited by 580 (1 self) - Add to MetaCart
This paper presents a statistical model which trains from a corpus annotated with Part-OfSpeech tags and assigns them to previously unseen text with state-of-the-art accuracy(96.6%). The model can be classified as a Maximum Entropy model and simultaneously uses many contextual "

On the algorithmic implementation of multi-class kernel-based vector machines

by Koby Crammer, Yoram Singer, Nello Cristianini, John Shawe-taylor, Bob Williamson - Journal of Machine Learning Research
"... In this paper we describe the algorithmic implementation of multiclass kernel-based vector machines. Our starting point is a generalized notion of the margin to multiclass problems. Using this notion we cast multiclass categorization problems as a constrained optimization problem with a quadratic ob ..."
Abstract - Cited by 559 (13 self) - Add to MetaCart
significant running time improvements for large datasets. Finally, we describe various experiments with our approach comparing it to previously studied kernel-based methods. Our experiments indicate that for multiclass problems we attain state-of-the-art accuracy.

Improving the Fisher kernel for large-scale image classification.

by Florent Perronnin , Jorge Sánchez , Thomas Mensink - In ECCV, , 2010
"... Abstract. The Fisher kernel (FK) is a generic framework which combines the benefits of generative and discriminative approaches. In the context of image classification the FK was shown to extend the popular bag-of-visual-words (BOV) by going beyond count statistics. However, in practice, this enric ..."
Abstract - Cited by 362 (20 self) - Add to MetaCart
demonstrate state-of-the-art accuracy on CalTech 256. A major advantage is that these results are obtained using only SIFT descriptors and costless linear classifiers. Equipped with this representation, we can now explore image classification on a larger scale. In the second part, as an application, we

Webspam Identification Through Content and Hyperlinks

by Jacob Abernethy, Olivier Chapelle, Carlos Castillo - Proc. Adversarial Information Retrieval on Web , 2008
"... We present an algorithm, witch, that learns to detect spam hosts or pages on the Web. Unlike most other approaches, it simultaneously exploits the structure of the Web graph as well as page contents and features. The method is efficient, scalable, and provides state-of-the-art accuracy on a standard ..."
Abstract - Cited by 26 (0 self) - Add to MetaCart
We present an algorithm, witch, that learns to detect spam hosts or pages on the Web. Unlike most other approaches, it simultaneously exploits the structure of the Web graph as well as page contents and features. The method is efficient, scalable, and provides state-of-the-art accuracy on a

WITCH: A New Approach to Web Spam Detection

by Jacob Abernethy, Olivier Chapelle, Carlos Castillo, Jacob Abernethy, Olivier Chapelle, Carlos Castillo - In Proceedings of the 4th International Workshop on Adversarial Information Retrieval on the Web (AIRWeb , 2008
"... ABSTRACT: We present an algorithm, witch, that learns to detect spam hosts or pages on the Web. Unlike most other approaches, it simultaneously exploits the structure of the Web graph as well as page contents and features. The method is efficient, scalable, and provides state-of-the-art accuracy on ..."
Abstract - Cited by 14 (1 self) - Add to MetaCart
ABSTRACT: We present an algorithm, witch, that learns to detect spam hosts or pages on the Web. Unlike most other approaches, it simultaneously exploits the structure of the Web graph as well as page contents and features. The method is efficient, scalable, and provides state-of-the-art accuracy

Graph Regularization Methods for Web Spam Detection

by Jacob Abernethy, Olivier Chapelle, Carlos Castillo
"... We present an algorithm, witch, that learns to detect spam hosts or pages on the Web. Unlike most other approaches, it simultaneously exploits the structure of the Web graph as well as page contents and features. The method is efficient, scalable, and provides state-of-the-art accuracy on a standar ..."
Abstract - Cited by 7 (0 self) - Add to MetaCart
We present an algorithm, witch, that learns to detect spam hosts or pages on the Web. Unlike most other approaches, it simultaneously exploits the structure of the Web graph as well as page contents and features. The method is efficient, scalable, and provides state-of-the-art accuracy on a

A Quick Guide to MaltParser Optimization

by Joakim Nivre, Johan Hall
"... MaltParser is a system for data-driven dependency parsing, which can be used to induce a parsing model from treebank data and to parse new data using an induced model. Parsers developed using MaltParser have achieved state-of-the-art accuracy for a number ..."
Abstract - Cited by 5 (2 self) - Add to MetaCart
MaltParser is a system for data-driven dependency parsing, which can be used to induce a parsing model from treebank data and to parse new data using an induced model. Parsers developed using MaltParser have achieved state-of-the-art accuracy for a number

Enforcing structural diversity in cube-pruned dependency parsing

by Hao Zhang, Ryan Mcdonald, Google Inc - In Proceedings of ACL. Association for Computational Linguistics , 2014
"... In this paper we extend the cube-pruned dependency parsing framework of Zhang et al. (2012; 2013) by forcing inference to maintain both label and structural ambigu-ity. The resulting parser achieves state-of-the-art accuracies, in particular on datasets with a large set of dependency labels. 1 ..."
Abstract - Cited by 4 (0 self) - Add to MetaCart
In this paper we extend the cube-pruned dependency parsing framework of Zhang et al. (2012; 2013) by forcing inference to maintain both label and structural ambigu-ity. The resulting parser achieves state-of-the-art accuracies, in particular on datasets with a large set of dependency labels. 1

Learning to Parse Natural Language with Maximum Entropy Models

by Adwait Ratnaparkhi , 1999
"... This paper presents a machine learning system for parsing natural language that learns from manually parsed example sentences, and parses unseen data at state-of-the-art accuracies. Its machine learning technology, based on the maximum entropy framework, is highly reusable and not specific to the pa ..."
Abstract - Cited by 191 (0 self) - Add to MetaCart
This paper presents a machine learning system for parsing natural language that learns from manually parsed example sentences, and parses unseen data at state-of-the-art accuracies. Its machine learning technology, based on the maximum entropy framework, is highly reusable and not specific

Image Classification using Super-Vector Coding of Local Image Descriptors

by Xi Zhou, Kai Yu, Tong Zhang, Thomas S. Huang
"... Abstract. This paper introduces a new framework for image classification using local visual descriptors. The pipeline first performs a nonlinear feature transformation on descriptors, then aggregates the results together to form image-level representations, and finally applies a classification model ..."
Abstract - Cited by 102 (2 self) - Add to MetaCart
model. For all the three steps we suggest novel solutions which make our approach appealing in theory, more scalable in computation, and transparent in classification. Our experiments demonstrate that the proposed classification method achieves state-of-the-art accuracy on the well-known PASCAL
Results 1 - 10 of 4,286