Weakly labeled data augmentation for social media named entity recognition

Abstract

Named entity recognition is a task that extracts entities corresponding to predefined categories. Although NER is important in processing user-generated texts such as those obtained from social media, it remains challenging because such texts tend to contain numerous unseen words or abbreviations. To address this issue, we propose two methods for weakly labeled data generation that can extract named entities from social media texts more effectively: alias augmentation and typo augmentation. Using these methods, weakly labeled data are generated through the automatic annotation of unlabeled Wikipedia texts and Tweets and then trained through transfer learning. Our experimental results suggest that the proposed approach improves NER performance, with our best F1-score of 51.43% representing the highest score ever reported.