Abstract:
Named Entity Recognition (NER) is the task of
classifying or labeling atomic elements in the text into
predefined sets of named entity categories such as
Person, Location, Organization or Number. NER is
also a crucial piece of Information Extraction System.
Robust handling of proper names is essential for many
applications in natural language processing and key
knowledge acquisition infrastructure for the Semantic
Web. For Myanmar Language, exploring features for
NER with machine learning approach is a still
challenging task because of the complex nature of the
language. This paper describes our effort on feature
exploring using local and external information for
Myanmar NER that applied Conditional Random
Fields (CRFs). The experimental results show that the
best result is obtained by combining the local feature,
such as neighboring words, and the external
information such as clue words and name lists.
