Part-Of-Speech Tagging
224 papers with code • 15 benchmarks • 26 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 |
Benchmarks
These leaderboards are used to track progress in Part-Of-Speech Tagging
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
Libraries
Use these libraries to find Part-Of-Speech Tagging models and implementationsDatasets
Penn Treebank
Universal Dependencies
CoNLL
CoNLL 2002
GUM
Most implemented papers
Towards Deep Learning Models Resistant to Adversarial Attacks
MadryLab/mnist_challenge
•
•
ICLR 2018
Its principled nature also enables us to identify methods for both training and attacking neural networks that are reliable and, in a certain sense, universal.
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
Most tasks in natural language processing can be cast into question answering (QA) problems over language input.
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.
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.
Does Manipulating Tokenization Aid Cross-Lingual Transfer? A Study on POS Tagging for Non-Standardized Languages
This can for instance be observed when finetuning PLMs on one language and evaluating them on data in a closely related language variety with no standardized orthography.
Part-of-Speech Tagging with Bidirectional Long Short-Term Memory Recurrent Neural Network
aneesh-joshi/LSTM_POS_Tagger
•
•
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.
Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networks
Recent papers have shown that neural networks obtain state-of-the-art performance on several different sequence tagging tasks.
Dice Loss for Data-imbalanced NLP Tasks
ShannonAI/dice_loss_for_NLP
•
•
ACL 2020
Many NLP tasks such as tagging and machine reading comprehension are faced with the severe data imbalance issue: negative examples significantly outnumber positive examples, and the huge number of background examples (or easy-negative examples) overwhelms the training.
Multilingual Part-of-Speech Tagging with Bidirectional Long Short-Term Memory Models and Auxiliary Loss
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.
