MUC 6

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 …...

This paper proposes a new approach for coreference resolution which uses the Bell tree to represent the search space and casts the coreference resolution problem as finding the best path …...

Most traditional information extraction approaches are generative models that assume events exist in text in certain patterns and these patterns can be regenerated in various ways. These assumptions limited the …...

This work applies boosted wrapper induction (BWI), a machine learning algorithm for informa- tion extraction from semi-structured documents, to the problem of named entity recognition. The de- fault feature set …...

Extraction of relations between entities is an important part of Information Extraction on free text. Previous methods are mostly based on statistical correlation and depend- ency relations between entities. This …...