Бустинг

Boosting is a machine learning algorithm that is not well known in computational linguistics. We ap- ply it to part-of-speech tagging and prepositional phrase attachment. Performance is very encourag- ing. …...

This paper investigates stacking and voting methods for combining strong classifiers like boosting, SVM, and TBL, on the named-entity recognition task. We demonstrate several ef- fective approaches, culminating in a …...

The focus of research in text classification has ex- panded from simple topic identification to more challenging tasks such as opinion/modality identi- fication. Unfortunately, the latter goals exceed the ability …...

Empirical experience and observations have shown us when powerful and highly tunable classifiers such as maximum en- tropy classifiers, boosting and SVMs are applied to language processing tasks, it is …...

This paper introduces a new application of boost- ing for parse reranking. Several parsers have been proposed that utilize the all-subtrees representa- tion (e.g., tree kernel and data oriented parsing). …...

This paper proposes a new discriminative training method, called minimum sample risk (MSR), of estimating parameters of language models for text input. While most existing discriminative training methods use a …...

This paper presents a comparative study of five parameter estimation algorithms on four NLP tasks. Three of the five algorithms are well-known in the computational linguistics community: Maximum Entropy (ME) …...

. Boosting is a general method for improving the accuracy of a given learning algorithm under certain restrictions. In this work, AdaBoost, one of the most popular boosting algorithms, is …...

In this paper, we present a simple and effective method to address the issue of how to generate diversified translation systems from a single Statistical Machine Translation (SMT) engine for …...

This paper describes our SeemGo sys- tem for the task of Aspect Based Sen- timent Analysis in SemEval-2014. The subtask of aspect term extraction is cast as a sequence labeling …...

AbstractWe present an efficient model selection method using boosting for transition-based constituency parsing. It is designed for exploring a high-dimensional search space, defined by a large set of feature templates, …...

AbstractNeural models have become one of the most important approaches to dialog response generation. However, they still tend to generate the most common and generic responses in the corpus all …...