Chunking, also known as shallow parsing, identifies continuous spans of tokens that form syntactic units such as noun phrases or verb phrases.
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We investigate the design challenges of constructing effective and efficient neural sequence labeling systems, by reproducing twelve neural sequence labeling models, which include most of the state-of-the-art structures, and conduct a systematic model comparison on three benchmarks (i. e. NER, Chunking, and POS tagging).
We propose a unified neural network architecture and learning algorithm that can be applied to various natural language processing tasks including: part-of-speech tagging, chunking, named entity recognition, and semantic role labeling.
We propose a sequence labeling framework with a secondary training objective, learning to predict surrounding words for every word in the dataset.
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It can also use sentence level tag information thanks to a CRF layer.
The results show that the proposed method achieves as good as or better results compared to the other word embeddings in the tasks we investigate.