Low Resource Multi-Task Sequence Tagging -- Revisiting Dynamic Conditional Random Fields

We compare different models for low resource multi-task sequence tagging that leverage dependencies between label sequences for different tasks. Our analysis is aimed at datasets where each example has labels for multiple tasks. Current approaches use either a separate model for each task or standard multi-task learning to learn shared feature representations. However, these approaches ignore correlations between label sequences, which can provide important information in settings with small training datasets. To analyze which scenarios can profit from modeling dependencies between labels in different tasks, we revisit dynamic conditional random fields (CRFs) and combine them with deep neural networks. We compare single-task, multi-task and dynamic CRF setups for three diverse datasets at both sentence and document levels in English and German low resource scenarios. We show that including silver labels from pretrained part-of-speech taggers as auxiliary tasks can improve performance on downstream tasks. We find that especially in low-resource scenarios, the explicit modeling of inter-dependencies between task predictions outperforms single-task as well as standard multi-task models

Biomedical Entity Recognition by Detection and Matching

Biomedical named entity recognition (BNER) serves as the foundation for numerous biomedical text mining tasks. Unlike general NER, BNER require a comprehensive grasp of the domain, and incorporating external knowledge beyond training data poses a significant challenge. In this study, we propose a novel BNER framework called DMNER. By leveraging existing entity representation models SAPBERT, we tackle BNER as a two-step process: entity boundary detection and biomedical entity matching. DMNER exhibits applicability across multiple NER scenarios: 1) In supervised NER, we observe that DMNER effectively rectifies the output of baseline NER models, thereby further enhancing performance. 2) In distantly supervised NER, combining MRC and AutoNER as span boundary detectors enables DMNER to achieve satisfactory results. 3) For training NER by merging multiple datasets, we adopt a framework similar to DS-NER but additionally leverage ChatGPT to obtain high-quality phrases in the training. Through extensive experiments conducted on 10 benchmark datasets, we demonstrate the versatility and effectiveness of DMNER.Comment: 9 pages content, 2 pages appendi

Data-efficient Active Learning for Structured Prediction with Partial Annotation and Self-Training

In this work we propose a pragmatic method that reduces the annotation cost for structured label spaces using active learning. Our approach leverages partial annotation, which reduces labeling costs for structured outputs by selecting only the most informative sub-structures for annotation. We also utilize self-training to incorporate the current model's automatic predictions as pseudo-labels for un-annotated sub-structures. A key challenge in effectively combining partial annotation with self-training to reduce annotation cost is determining which sub-structures to select to label. To address this challenge, we adopt an error estimator to adaptively decide the partial selection ratio according to the current model's capability. In evaluations spanning four structured prediction tasks, we show that our combination of partial annotation and self-training using an adaptive selection ratio reduces annotation cost over strong full annotation baselines under a fair comparison scheme that takes reading time into consideration.Comment: Findings of EMNLP 202

Extracting health information from social media

Social media platforms with large user bases such as Twitter, Reddit, and online health forums contain a rich amount of health-related information. Despite the advances achieved in natural language processing (NLP), extracting actionable health information from social media still remains challenging. This thesis proposes a set of methodologies that can be used to extract medical concepts and health information from social media that is related to drugs, symptoms, and side-effects. We first develop a rule-based relationship extraction system that utilises a set of dictionaries and linguistic rules in order to extract structured information from patients’ posts on online health forums. We then automate the concept extraction pro-cess via; i) a supervised algorithm that has been trained with a small labelled dataset, and ii) an iterative semi-supervised algorithm capable of learning new sentences and concepts. We test our machine-learning pipeline on a COVID-19 case study that involves patient authored social media posts. We develop a novel triage and diagnostic approach to extract symptoms, severity, and prevalence of the disease rather than to provide any actionable decisions at the individual level. Finally, we extend our approach by investigating the potential benefit of incorporating dictionary information into a neural network architecture for natural language processing