CoNLL03

In machine learning, whether one can build a more accurate classifier by using unlabeled data (semi-supervised learning) is an important issue. Although a num- ber of semi-supervised methods have been …...

This paper provides evidence that the use of more unlabeled data in semi-supervised learn- ing can improve the performance of Natu- ral Language Processing (NLP) tasks, such as part-of-speech tagging, …...

Extracting named entities in text and link- ing extracted names to a given knowledge base are fundamental tasks in applications for text understanding. Existing systems typically run a named entity …...

AbstractMethods for Named Entity Recognition and Disambiguation (NERD) perform NER and NED in two separate stages. Therefore, NED may be penalized with respect to precision by NER false positives, and …...

AbstractContextual string embeddings are a recent type of contextualized word embedding that were shown to yield state-of-the-art results when utilized in a range of sequence labeling tasks. They are based …...

AbstractMany NLP tasks such as tagging and machine reading comprehension are faced with the severe data imbalance issue: negative examples significantly outnumber positive examples, and the huge number of easy-negative …...

AbstractNamed Entity Recognition (NER) remains difficult in real-world settings; current challenges include short texts (low context), emerging entities, and complex entities (e.g. movie names). Gazetteer features can help, but results …...

AbstractRecent information extraction approaches have relied on training deep neural models. However, such models can easily overfit noisy labels and suffer from performance degradation. While it is very costly to …...

There is a recent interest in investigating few- shot NER, where the low-resource target do- main has different label sets compared with a resource-rich source domain. Existing meth- ods use …...

AbstractNamed Entity Recognition (NER) in Few-Shot setting is imperative for entity tagging in low resource domains. Existing approaches only learn class-specific semantic features and intermediate representations from source domains. This …...

AbstractTraditional methods for named entity recognition (NER) classify mentions into a fixed set of pre-defined entity types. However, in many real-world scenarios, new entity types are incrementally involved. To investigate …...