Improving Named Entity Recognition in Telephone Conversations via Effective Active Learning with Human in the Loop

11/02/2022
by   Md Tahmid Rahman Laskar, et al.
0

Telephone transcription data can be very noisy due to speech recognition errors, disfluencies, etc. Not only that annotating such data is very challenging for the annotators, but also such data may have lots of annotation errors even after the annotation job is completed, resulting in a very poor model performance. In this paper, we present an active learning framework that leverages human in the loop learning to identify data samples from the annotated dataset for re-annotation that are more likely to contain annotation errors. In this way, we largely reduce the need for data re-annotation for the whole dataset. We conduct extensive experiments with our proposed approach for Named Entity Recognition and observe that by re-annotating only about 6 training instances out of the whole dataset, the F1 score for a certain entity type can be significantly improved by about 25

READ FULL TEXT

page 1

page 2

page 3

page 4

research
11/26/2021

KazNERD: Kazakh Named Entity Recognition Dataset

We present the development of a dataset for Kazakh named entity recognit...
research
08/28/2020

Cost-Quality Adaptive Active Learning for Chinese Clinical Named Entity Recognition

Clinical Named Entity Recognition (CNER) aims to automatically identity ...
research
10/01/2021

OPAD: An Optimized Policy-based Active Learning Framework for Document Content Analysis

Documents are central to many business systems, and include forms, repor...
research
10/06/2019

Named Entity Recognition – Is there a glass ceiling?

Recent developments in Named Entity Recognition (NER) have resulted in b...
research
06/26/2018

A Practical Incremental Learning Framework For Sparse Entity Extraction

This work addresses challenges arising from extracting entities from tex...
research
08/16/2021

Partially Supervised Named Entity Recognition via the Expected Entity Ratio Loss

We study learning named entity recognizers in the presence of missing en...
research
05/21/2013

Robust Logistic Regression using Shift Parameters (Long Version)

Annotation errors can significantly hurt classifier performance, yet dat...

Please sign up or login with your details

Forgot password? Click here to reset