Part-of-speech tagging (POS tagging) is the task of tagging a word in a text with its part of speech. A part of speech is a category of words with similar grammatical properties. Common English parts of speech are noun, verb, adjective, adverb, pronoun, preposition, conjunction, etc.
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We therefore propose Cross-View Training (CVT), a semi-supervised learning algorithm that improves the representations of a Bi-LSTM sentence encoder using a mix of labeled and unlabeled data.
Ranked #1 on CCG Supertagging on CCGBank
Our model is a simple feed-forward neural network that operates on a task-specific transition system, yet achieves comparable or better accuracies than recurrent models.
Ranked #15 on Dependency Parsing on Penn Treebank
We show that the use of web crawled data is preferable to the use of Wikipedia data.
Ranked #1 on Dependency Parsing on Spoken Corpus
Recent advances in language modeling using recurrent neural networks have made it viable to model language as distributions over characters.
One of the keys to enable chatbots to communicate with human in a more natural way is the ability to handle long and complex user's utterances.
We introduce N-LTP, an open-source Python Chinese natural language processing toolkit supporting five basic tasks: Chinese word segmentation, part-of-speech tagging, named entity recognition, dependency parsing, and semantic dependency parsing.
State-of-the-art sequence labeling systems traditionally require large amounts of task-specific knowledge in the form of hand-crafted features and data pre-processing.
Ranked #5 on Named Entity Recognition on CoNLL++
Neural NLP systems achieve high scores in the presence of sizable training dataset.