Partial
Annotation Scheme for Active Learning on Named Entity Recognition Tasks
(pp319-332)
Koga Kobayashi and Kei Wakabayashi
doi:
https://doi.org/10.26421/JDI1.3-2
Abstracts:
Active learning is a
promising approach to alleviate the expensive annotation cost for
making training data on named entity recognition (NER)
tasks. However, since existing active learning methods on
NER
tasks implicitly assume the full annotation scheme of which the unit
of an annotation request is the whole sentence, the efficiency of
the data instance selection is limited. In this paper, we propose a
new active learning method based on a partial annotation scheme,
which selects a part of the sentences to be annotated and asks human
annotators to label a specific part of the target sentences. In the
experiment, we show that the partial annotation scheme can quickly
train the proposed point-wise prediction model compared to the
existing active learning methods on
NER
tasks.
Key words: Neural networks, Named entity
recognition, Text tagging