Entity Recognition from Colloquial Text

Abstract

Extraction of concepts and entities of interest from non-formal texts such as social media posts and informal communication is an important capability for decision support systems in many domains, including healthcare, customer relationship management, and others. Despite the recent advances in training large language models for a variety of natural language processing tasks, the developed models and techniques have mainly focused on formal texts and do not perform as well on colloquial data, which is characterized by a number of distinct challenges. In our research, we focus on the healthcare domain and investigate the problem of symptom recognition from colloquial texts by designing and evaluating several training strategies for BERT-based model fine-tuning. These strategies are distinguished by the choice of the base model, the training corpora, and application of term perturbations in the training data. The best-performing models trained using these strategies outperform the state-of-the-art specialized symptom recognizer by a large margin. Through a series of experiments, we have found specific patterns of model behavior associated with the training strategies we designed. We present design principles for training strategies for effective entity recognition in colloquial texts based on our findings.

References in corpus (7)

• Explaining and Harnessing Adversarial Examples
• LLaMA: Open and Efficient Foundation Language Models
• LoRA: Low-Rank Adaptation of Large Language Models
• Introduction to the CoNLL-2002 Shared Task: Language-Independent Named Entity Recognition
• Publicly Available Clinical BERT Embeddings
• A Survey on Recent Advances in Named Entity Recognition from Deep Learning models
• On Adversarial Examples for Biomedical NLP Tasks