11,685 research outputs found
Hierarchical Contextualized Representation for Named Entity Recognition
Named entity recognition (NER) models are typically based on the architecture
of Bi-directional LSTM (BiLSTM). The constraints of sequential nature and the
modeling of single input prevent the full utilization of global information
from larger scope, not only in the entire sentence, but also in the entire
document (dataset). In this paper, we address these two deficiencies and
propose a model augmented with hierarchical contextualized representation:
sentence-level representation and document-level representation. In
sentence-level, we take different contributions of words in a single sentence
into consideration to enhance the sentence representation learned from an
independent BiLSTM via label embedding attention mechanism. In document-level,
the key-value memory network is adopted to record the document-aware
information for each unique word which is sensitive to similarity of context
information. Our two-level hierarchical contextualized representations are
fused with each input token embedding and corresponding hidden state of BiLSTM,
respectively. The experimental results on three benchmark NER datasets
(CoNLL-2003 and Ontonotes 5.0 English datasets, CoNLL-2002 Spanish dataset)
show that we establish new state-of-the-art results.Comment: Accepted by AAAI 202
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
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