Domain Adaptation for Named Entity Recognition in Online Media with Word Embeddings
This addresses the challenge of domain adaptation for NER in online media, enabling better entity identification across diverse content, though it is incremental as it builds on existing word embedding techniques.
The paper tackles the problem of Named Entity Recognition (NER) models failing to generalize across domains like Sports and Finance due to linguistic variations and scarce annotated data, proposing methods using word embeddings to adapt models from data-rich domains to data-scarce ones, resulting in models that outperform previous baselines.
Content on the Internet is heterogeneous and arises from various domains like News, Entertainment, Finance and Technology. Understanding such content requires identifying named entities (persons, places and organizations) as one of the key steps. Traditionally Named Entity Recognition (NER) systems have been built using available annotated datasets (like CoNLL, MUC) and demonstrate excellent performance. However, these models fail to generalize onto other domains like Sports and Finance where conventions and language use can differ significantly. Furthermore, several domains do not have large amounts of annotated labeled data for training robust Named Entity Recognition models. A key step towards this challenge is to adapt models learned on domains where large amounts of annotated training data are available to domains with scarce annotated data. In this paper, we propose methods to effectively adapt models learned on one domain onto other domains using distributed word representations. First we analyze the linguistic variation present across domains to identify key linguistic insights that can boost performance across domains. We propose methods to capture domain specific semantics of word usage in addition to global semantics. We then demonstrate how to effectively use such domain specific knowledge to learn NER models that outperform previous baselines in the domain adaptation setting.