3 research outputs found
Few-shot classification in Named Entity Recognition Task
For many natural language processing (NLP) tasks the amount of annotated data
is limited. This urges a need to apply semi-supervised learning techniques,
such as transfer learning or meta-learning. In this work we tackle Named Entity
Recognition (NER) task using Prototypical Network - a metric learning
technique. It learns intermediate representations of words which cluster well
into named entity classes. This property of the model allows classifying words
with extremely limited number of training examples, and can potentially be used
as a zero-shot learning method. By coupling this technique with transfer
learning we achieve well-performing classifiers trained on only 20 instances of
a target class.Comment: In proceedings of the 34th ACM/SIGAPP Symposium on Applied Computin
Improved named entity recognition using machine translation-based cross-lingual information
In this paper, we describe a technique to improve named entity recognition in a resource-poor language (Hindi) by using cross-lingual information. We
use an on-line machine translation system and a separate word alignment phase
to find the projection of each Hindi word into the translated English sentence. We
estimate the cross-lingual features using an English named entity recognizer and
the alignment information. We use these cross-lingual features in a support vector
machine-based classifier. The use of cross-lingual features improves F1 score by
2.1 points absolute (2.9% relative) over a good-performing baseline model