Combining proper name-coreference with conditional random fields for semi-supervised named entity recognition in Vietnamese text

Abstract

Named entity recognition (NER) is the process of seeking to locate atomic elements in text into predefined categories such as the names of persons, organizations and locations. Most existing NER systems are based on supervised learning. This method often requires a large amount of labelled training data, which is very time-consuming to build. To solve this problem, we introduce a semi-supervised learning method for recognizing named entities in Vietnamese text by combining proper name coreference, named-ambiguity heuristics with a powerful sequential learning model, Conditional Random Fields. Our approach inherits the idea of Liao and Veeramachaneni [6] and expands it by using proper name coreference. Starting by training the model using a small data set that is annotated manually, the learning model extracts high confident named entities and finds low confident ones by using proper name coreference rules. The low confident named entities are put in the training set to learn new context features. The F-scores of the system for extracting "Person", "Location" and "Organization"entities are 83.36%, 69.53% and 65.71% when applying heuristics proposed by Liao and Veeramachaneni. Those values when using our proposed heuristics are 93.13%, 88.15% and 79.35%, respectively. It shows that our method is good in increasing the system accuracy. © 2011 Springer-Verlag.