Browse > Natural Language Processing > Part-Of-Speech Tagging

Part-Of-Speech Tagging

62 papers with code · Natural Language Processing

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

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Greatest papers with code

Globally Normalized Transition-Based Neural Networks

ACL 2016 tensorflow/models

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 results. 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 SENTENCE COMPRESSION

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

ACL 2016 guillaumegenthial/sequence_tagging

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. In this paper, we introduce a novel neutral network architecture that benefits from both word- and character-level representations automatically, by using combination of bidirectional LSTM, CNN and CRF.

NAMED ENTITY RECOGNITION PART-OF-SPEECH TAGGING

NCRF++: An Open-source Neural Sequence Labeling Toolkit

ACL 2018 jiesutd/NCRFpp

This paper describes NCRF++, a toolkit for neural sequence labeling. NCRF++ is designed for quick implementation of different neural sequence labeling models with a CRF inference layer.

CHUNKING NAMED ENTITY RECOGNITION PART-OF-SPEECH TAGGING

Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networks

18 Mar 2017jiesutd/NCRFpp

Recent papers have shown that neural networks obtain state-of-the-art performance on several different sequence tagging tasks. One appealing property of such systems is their generality, as excellent performance can be achieved with a unified architecture and without task-specific feature engineering.

NAMED ENTITY RECOGNITION PART-OF-SPEECH TAGGING TRANSFER LEARNING

Chinese Lexical Analysis with Deep Bi-GRU-CRF Network

5 Jul 2018baidu/lac

Lexical analysis is believed to be a crucial step towards natural language understanding and has been widely studied. Recent years, end-to-end lexical analysis models with recurrent neural networks have gained increasing attention.

LEXICAL ANALYSIS NAMED ENTITY RECOGNITION PART-OF-SPEECH TAGGING

Empower Sequence Labeling with Task-Aware Neural Language Model

13 Sep 2017LiyuanLucasLiu/LM-LSTM-CRF

In this study, we develop a novel neural framework to extract abundant knowledge hidden in raw texts to empower the sequence labeling task. Transfer learning techniques are further adopted to mediate different components and guide the language model towards the key knowledge.

LANGUAGE MODELLING NAMED ENTITY RECOGNITION PART-OF-SPEECH TAGGING TRANSFER LEARNING WORD EMBEDDINGS

Natural Language Processing (almost) from Scratch

2 Mar 2011facebook/fb-caffe-exts

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.

CHUNKING NAMED ENTITY RECOGNITION PART-OF-SPEECH TAGGING SEMANTIC ROLE LABELING

Semi-supervised Multitask Learning for Sequence Labeling

ACL 2017 marekrei/sequence-labeler

We propose a sequence labeling framework with a secondary training objective, learning to predict surrounding words for every word in the dataset. This language modeling objective incentivises the system to learn general-purpose patterns of semantic and syntactic composition, which are also useful for improving accuracy on different sequence labeling tasks.

CHUNKING GRAMMATICAL ERROR DETECTION LANGUAGE MODELLING NAMED ENTITY RECOGNITION PART-OF-SPEECH TAGGING