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 "

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.

This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multcriteria ratings, and a provision of more flexible and less intrusive types of recommendations.

Solving optimization problems with multiple (often conflicting) objectives is, generally, a very difficult goal. Evolutionary algorithms (EAs) were initially extended and applied during the mid-eighties in an attempt to stochastically solve problems of this generic class. During the past decade, a variety of multiobjective EA (MOEA) techniques have been proposed and applied to many scientific and engineering applications. Our discussion's intent is to rigorously define multiobjective optimization problems and certain related concepts, present an MOEA classification scheme, and evaluate the variety of contemporary MOEAs. Current MOEA theoretical developments are evaluated; specific topics addressed include fitness functions, Pareto ranking, niching, fitness sharing, mating restriction, and secondary populations. Since the development and application of MOEAs is a dynamic and rapidly growing activity, we focus on key analytical insights based upon critical MOEA evaluation of c...

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

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

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

representation has not yet shown its superiority over the BOV. In the first part we show that with several well-motivated modifications over the original framework we can boost the accuracy of the FK. On PASCAL VOC 2007 we increase the Average Precision (AP) from 47.9 % to 58.3%. Similarly, we demonstrate state-of-the-art

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

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