This paper presents a classifier-combination experimental framework for named entity
recognition in which four diverse classifiers (robust linear classifier, maximum entropy …

Abstract We apply Support Vector Machines (SVMs) to identify English base phrases
(chunks). SVMs are known to achieve high generalization performance even with input data …

Named entity recognition (NER) is frequently addressed as a sequence classification task
where each input consists of one sentence of text. It is nevertheless clear that useful …

Lexical-semantic resources, including thesauri and WORDNET, have been successfully
incorporated into a wide range of applications in Natural Language Processing. However …

We present a unified technique to solve different shallow parsing tasks as a tagging problem
using a Hidden Markov Model-based approach (HMM). This technique consists of the …

We present memory-based learning approaches to shallow parsing and apply these to five
tasks: base noun phrase identification, arbitrary base phrase recognition, clause detection …

This paper considers the hypothesis that voting between multiple data representations can
be more accurate than voting between multiple learning models. This hypothesis has been …

This paper demonstrates the substantial empirical success of classifier combination for the
word sense disambiguation task. It investigates more than 10 classifier combination …

Abstract Machine-generated documents containing semi-structured text are rapidly forming
the bulk of data being stored in an organisation. Given a feature-based representation of …

This paper proposes an unsupervised learning model for classifying named entities. This
model uses a training set, built automatically by means of a small-scale named entity …