Named entity recognition (NER) is the task to identify mentions of rigid designators from text
belonging to predefined semantic types such as person, location, organization etc. NER …

With the advent of Web 2.0, there exist many online platforms that result in massive textual-
data production. With ever-increasing textual data at hand, it is of immense importance to …

Recently, the character-word lattice structure has been proved to be effective for Chinese
named entity recognition (NER) by incorporating the word information. However, since the …

Abstract Language model pre-training has proven to be useful in learning universal
language representations. As a state-of-the-art language model pre-training model, BERT …

Recent advances in language modeling using recurrent neural networks have made it
viable to model language as distributions over characters. By learning to predict the next …

Inductive transfer learning has greatly impacted computer vision, but existing approaches in
NLP still require task-specific modifications and training from scratch. We propose Universal …

In this paper, we investigate the modeling power of contextualized embeddings from pre-
trained language models, eg BERT, on the E2E-ABSA task. Specifically, we build a series of …

We investigate a lattice-structured LSTM model for Chinese NER, which encodes a
sequence of input characters as well as all potential words that match a lexicon. Compared …

The Bidirectional long short-term memory networks (BiLSTM) have been widely used as an
encoder in models solving the named entity recognition (NER) task. Recently, the …

Natural Language Processing (NLP) helps empower intelligent machines by enhancing a
better understanding of the human language for linguistic-based human-computer …