Adaptive Name Entity Recognition under Highly Unbalanced Data

TL;DR

This paper proposes an adaptive neural architecture for Named Entity Recognition that addresses class imbalance by splitting data into weak and strong classes, significantly improving recognition of underrepresented entities.

Contribution

The study introduces a novel approach of class-based model adaptation for NER, effectively handling highly unbalanced datasets with minimal data for weak classes.

Findings

Improved recognition of weak classes using small data sets.

Fusion of embeddings enhances model generalization.

Adaptive splitting boosts overall NER performance.

Abstract

For several purposes in Natural Language Processing (NLP), such as Information Extraction, Sentiment Analysis or Chatbot, Named Entity Recognition (NER) holds an important role as it helps to determine and categorize entities in text into predefined groups such as the names of persons, locations, quantities, organizations or percentages, etc. In this report, we present our experiments on a neural architecture composed of a Conditional Random Field (CRF) layer stacked on top of a Bi-directional LSTM (BI-LSTM) layer for solving NER tasks. Besides, we also employ a fusion input of embedding vectors (Glove, BERT), which are pre-trained on the huge corpus to boost the generalization capacity of the model. Unfortunately, due to the heavy unbalanced distribution cross-training data, both approaches just attained a bad performance on less training samples classes. To overcome this challenge, we introduce an add-on classification model to split sentences into two different sets: Weak and Strong classes and then designing a couple of Bi-LSTM-CRF models properly to optimize performance on each set. We evaluated our models on the test set and discovered that our method can improve performance for Weak classes significantly by using a very small data set (approximately 0.45\%) compared to the rest classes.