Over the past few years, neural networks have re-emerged as powerful machine-learning
models, yielding state-of-the-art results in fields such as image recognition and speech …

Sequence-to-Sequence (seq2seq) modeling has rapidly become an important general-
purpose NLP tool that has proven effective for many text-generation and sequence-labeling …

We introduce a globally normalized transition-based neural network model that achieves
state-of-the-art part-of-speech tagging, dependency parsing and sentence compression …

Syntactic and semantic parsing has been investigated for decades, which is one primary
topic in the natural language processing community. This article aims for a brief survey on …

Neural networks have shown promising results for relation extraction. State-of-the-art
models cast the task as an end-to-end problem, solved incrementally using a local classifier …

Unlike widely used Named Entity Recognition (NER) data sets in generic domains,
biomedical NER data sets often contain mentions consisting of discontinuous spans …

It is common that entity mentions can contain other mentions recursively. This paper
introduces a scalable transition-based method to model the nested structure of mentions …

Neural machine translation (NMT) with recurrent neural networks, has proven to be an
effective technique for end-to-end machine translation. However, in spite of its promising …

Character-based and word-based methods are two main types of statistical models for
Chinese word segmentation, the former exploiting sequence labeling models over …

Parsing accuracy using efficient greedy transition systems has improved dramatically in
recent years thanks to neural networks. Despite striking results in dependency parsing …