6,426 research outputs found
Modelling Instance-Level Annotator Reliability for Natural Language Labelling Tasks
When constructing models that learn from noisy labels produced by multiple
annotators, it is important to accurately estimate the reliability of
annotators. Annotators may provide labels of inconsistent quality due to their
varying expertise and reliability in a domain. Previous studies have mostly
focused on estimating each annotator's overall reliability on the entire
annotation task. However, in practice, the reliability of an annotator may
depend on each specific instance. Only a limited number of studies have
investigated modelling per-instance reliability and these only considered
binary labels. In this paper, we propose an unsupervised model which can handle
both binary and multi-class labels. It can automatically estimate the
per-instance reliability of each annotator and the correct label for each
instance. We specify our model as a probabilistic model which incorporates
neural networks to model the dependency between latent variables and instances.
For evaluation, the proposed method is applied to both synthetic and real data,
including two labelling tasks: text classification and textual entailment.
Experimental results demonstrate our novel method can not only accurately
estimate the reliability of annotators across different instances, but also
achieve superior performance in predicting the correct labels and detecting the
least reliable annotators compared to state-of-the-art baselines.Comment: 9 pages, 1 figures, 10 tables, 2019 Annual Conference of the North
American Chapter of the Association for Computational Linguistics (NAACL2019
Adversarial Learning for Chinese NER from Crowd Annotations
To quickly obtain new labeled data, we can choose crowdsourcing as an
alternative way at lower cost in a short time. But as an exchange, crowd
annotations from non-experts may be of lower quality than those from experts.
In this paper, we propose an approach to performing crowd annotation learning
for Chinese Named Entity Recognition (NER) to make full use of the noisy
sequence labels from multiple annotators. Inspired by adversarial learning, our
approach uses a common Bi-LSTM and a private Bi-LSTM for representing
annotator-generic and -specific information. The annotator-generic information
is the common knowledge for entities easily mastered by the crowd. Finally, we
build our Chinese NE tagger based on the LSTM-CRF model. In our experiments, we
create two data sets for Chinese NER tasks from two domains. The experimental
results show that our system achieves better scores than strong baseline
systems.Comment: 8 pages, AAAI-201
A plea for more interactions between psycholinguistics and natural language processing research
A new development in psycholinguistics is the use of regression analyses on tens of thousands of words, known as the megastudy approach. This development has led to the collection of processing times and subjective ratings (of age of acquisition, concreteness, valence, and arousal) for most of the existing words in English and Dutch. In addition, a crowdsourcing study in the Dutch language has resulted in information about how well 52,000 lemmas are known. This information is likely to be of interest to NLP researchers and computational linguists. At the same time, large-scale measures of word characteristics developed in the latter traditions are likely to be pivotal in bringing the megastudy approach to the next level
- …