Abstract: 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. This versatility is achieved by trying to avoid task-specific engineering and therefore disregarding a lot of prior knowledge. Instead of exploiting man-made input features carefully optimized for each task, our system learns internal representations on the basis of vast amounts of mostly unlabeled training data. This work is then used as a basis for building a freely available tagging system with good performance and minimal computational requirements.
@article{collobert-2011,
author = {Collobert, Ronan and Weston, Jason and Bottou, L\'eon and Karlen, Michael and Kavukcuoglu, Koray and Kuksa, Pavel},
title = {Natural Language Processing (Almost) from Scratch},
journal = {Journal of Machine Learning Research},
year = {2011},
volume = {12},
pages = {2493--2537},
month = {Aug},
url = {http://leon.bottou.org/papers/collobert-2011},
}
The universal natural language tagger described in this paper is actively maintained by Ronan Collobert. It can be downloaded from Senna. Besides part-of-speech tagging, chunking, named entity extraction, and semantic role labelling, the latest version also outputs syntactic parse trees, still using the neural network architecture described in this paper.