Part-Of-Speech Tagging

181 papers with code • 16 benchmarks • 22 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

Libraries

Use these libraries to find Part-Of-Speech Tagging models and implementations
2 papers
1,837

Most implemented papers

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.

Ask Me Anything: Dynamic Memory Networks for Natural Language Processing

DongjunLee/dmn-tensorflow 24 Jun 2015

Most tasks in natural language processing can be cast into question answering (QA) problems over language input.

ZEN: Pre-training Chinese Text Encoder Enhanced by N-gram Representations

sinovation/ZEN Findings of the Association for Computational Linguistics 2020

Moreover, it is shown that reasonable performance can be obtained when ZEN is trained on a small corpus, which is important for applying pre-training techniques to scenarios with limited data.

CamemBERT: a Tasty French Language Model

huggingface/transformers ACL 2020

We show that the use of web crawled data is preferable to the use of Wikipedia data.

Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networks

kimiyoung/transfer 18 Mar 2017

Recent papers have shown that neural networks obtain state-of-the-art performance on several different sequence tagging tasks.

Part-of-Speech Tagging with Bidirectional Long Short-Term Memory Recurrent Neural Network

aneesh-joshi/LSTM_POS_Tagger 21 Oct 2015

Bidirectional Long Short-Term Memory Recurrent Neural Network (BLSTM-RNN) has been shown to be very effective for tagging sequential data, e. g. speech utterances or handwritten documents.

Multilingual Part-of-Speech Tagging with Bidirectional Long Short-Term Memory Models and Auxiliary Loss

bplank/bilstm-aux ACL 2016

Bidirectional long short-term memory (bi-LSTM) networks have recently proven successful for various NLP sequence modeling tasks, but little is known about their reliance to input representations, target languages, data set size, and label noise.

Semi-supervised Multitask Learning for Sequence Labeling

marekrei/sequence-labeler ACL 2017

We propose a sequence labeling framework with a secondary training objective, learning to predict surrounding words for every word in the dataset.

Empower Sequence Labeling with Task-Aware Neural Language Model

LiyuanLucasLiu/LM-LSTM-CRF 13 Sep 2017

In this study, we develop a novel neural framework to extract abundant knowledge hidden in raw texts to empower the sequence labeling task.

Learning Approximate Inference Networks for Structured Prediction

lifu-tu/ENGINE ICLR 2018

Prior work used gradient descent for inference, relaxing the structured output to a set of continuous variables and then optimizing the energy with respect to them.