MUC 7

Most machine learning solutions to noun phrase coreference resolution recast the problem as a classification task. We ex- amine three potential problems with this reformulation, namely, skewed class dis- tributions, …...

This paper proposes a Hidden Markov Model (HMM) and an HMM-based chunk tagger, from which a named entity (NE) recognition (NER) system is built to recognize and classify names, times …...

This paper presents a maximum entropy-based named entity recognizer (NER). It differs from pre- vious machine learning-based NERs in that it uses information from the whole document to classify each …...

In this paper we propose a competition learning approach to coreference resolu- tion. Traditionally, supervised machine learning approaches adopt the single- candidate model. Nevertheless the prefer- ence relationship between the …...

This paper presents a constraint-based multi- agent strategy to coreference resolution of general noun phrases in unrestricted English text. For a given anaphor and all the preceding referring expressions as …...

We describe a generative model for clustering named entities which also models named en- tity internal structure, clustering related words by role. The model is entirely unsupervised; it uses features …...