Part-Of-Speech Tagging

163 papers with code • 12 benchmarks • 14 datasets

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.

Example:

Vinken , 61 years old
NNP , CD NNS JJ

Greatest papers with code

Semi-Supervised Sequence Modeling with Cross-View Training

tensorflow/models EMNLP 2018

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.

CCG Supertagging Dependency Parsing +6

Globally Normalized Transition-Based Neural Networks

tensorflow/models ACL 2016

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.

Dependency Parsing Part-Of-Speech Tagging +1

Contextual String Embeddings for Sequence Labeling

zalandoresearch/flair COLING 2018

Recent advances in language modeling using recurrent neural networks have made it viable to model language as distributions over characters.

Chunking Language Modelling +3

N-LTP: An Open-source Neural Language Technology Platform for Chinese

HIT-SCIR/ltp EMNLP (ACL) 2021

We introduce \texttt{N-LTP}, an open-source neural language technology platform supporting six fundamental Chinese NLP tasks: {lexical analysis} (Chinese word segmentation, part-of-speech tagging, and named entity recognition), {syntactic parsing} (dependency parsing), and {semantic parsing} (semantic dependency parsing and semantic role labeling).

Chinese Word Segmentation Dependency Parsing +6

Chinese Lexical Analysis with Deep Bi-GRU-CRF Network

baidu/lac 5 Jul 2018

Lexical analysis is believed to be a crucial step towards natural language understanding and has been widely studied.

Language understanding Lexical Analysis +3

End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF

guillaumegenthial/sequence_tagging ACL 2016

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.

Feature Engineering Named Entity Recognition +3