This paper describes an approach for automatic construction of dictionaries for Named Entity Recognition (NER) using large amounts of unlabeled data and a few seed examples. We use Canonical Correlation Analysis (CCA) to obtain lower dimensional embeddings (representations) for candidate phrases and classify these phrases using a small number of labeled examples. Our method achieves 16.5 and 11.3 F-1 score improvement over co-training on disease and virus NER respectively. We also show that by adding candidate phrase embeddings as features in a sequence tagger gives better performance compared to using word embeddings.
In other works such as and external structured resources like Wikipedia have been used to construct dictionaries. Even though these methods are fairly successful they suffer from a number of drawbacks especially in the biomedical domain. The main drawback of these approaches is that it is very difficult to accurately disambiguate ambiguous entities especially when the entities are abbreviations @cite_9 . For example, is the abbreviation for the disease and the disambiguation page for in Wikipedia associates it to more than 50 categories since can be expanded to , , and so on, each of it belonging to a different category. Due to the rapid growth of Wikipedia, the number of entities that have disambiguation pages is growing fast and it is increasingly difficult to retrieve the article we want. Also, it is tough to understand these approaches from a theoretical standpoint.
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Models for many natural language tasks benefit from the flexibility to use overlapping, non-independent features. For example, the need for labeled data can be drastically reduced by taking advantage of domain knowledge in the form of word lists, part-of-speech tags, character n-grams, and capitalization patterns. While it is difficult to capture such inter-dependent features with a generative probabilistic model, conditionally-trained models, such as conditional maximum entropy models, handle them well. There has been significant work with such models for greedy sequence modeling in NLP (Ratnaparkhi, 1996; , 1998).