William Campbell


pdf bib
Meta-Learning for Few-Shot Named Entity Recognition
Cyprien de Lichy | Hadrien Glaude | William Campbell
Proceedings of the 1st Workshop on Meta Learning and Its Applications to Natural Language Processing

Meta-learning has recently been proposed to learn models and algorithms that can generalize from a handful of examples. However, applications to structured prediction and textual tasks pose challenges for meta-learning algorithms. In this paper, we apply two meta-learning algorithms, Prototypical Networks and Reptile, to few-shot Named Entity Recognition (NER), including a method for incorporating language model pre-training and Conditional Random Fields (CRF). We propose a task generation scheme for converting classical NER datasets into the few-shot setting, for both training and evaluation. Using three public datasets, we show these meta-learning algorithms outperform a reasonable fine-tuned BERT baseline. In addition, we propose a novel combination of Prototypical Networks and Reptile.


pdf bib
Graph-Based Semi-Supervised Learning for Natural Language Understanding
Zimeng Qiu | Eunah Cho | Xiaochun Ma | William Campbell
Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)

Semi-supervised learning is an efficient method to augment training data automatically from unlabeled data. Development of many natural language understanding (NLU) applications has a challenge where unlabeled data is relatively abundant while labeled data is rather limited. In this work, we propose transductive graph-based semi-supervised learning models as well as their inductive variants for NLU. We evaluate the approach’s applicability using publicly available NLU data and models. In order to find similar utterances and construct a graph, we use a paraphrase detection model. Results show that applying the inductive graph-based semi-supervised learning can improve the error rate of the NLU model by 5%.


pdf bib
Content+Context=Classification: Examining the Roles of Social Interactions and Linguist Content in Twitter User Classification
William Campbell | Elisabeth Baseman | Kara Greenfield
Proceedings of the Second Workshop on Natural Language Processing for Social Media (SocialNLP)