Part-Of-Speech Tagging
214 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
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Libraries
Use these libraries to find Part-Of-Speech Tagging models and implementationsDatasets
Penn Treebank
Universal Dependencies
CoNLL
CoNLL 2002
GUM
Most implemented papers
Morphosyntactic Tagging with a Meta-BiLSTM Model over Context Sensitive Token Encodings
google/meta_tagger
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ACL 2018
In this paper, we investigate models that use recurrent neural networks with sentence-level context for initial character and word-based representations.
Chinese Lexical Analysis with Deep Bi-GRU-CRF Network
baidu/lac
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•
Lexical analysis is believed to be a crucial step towards natural language understanding and has been widely studied.
LemmaTag: Jointly Tagging and Lemmatizing for Morphologically-Rich Languages with BRNNs
We present LemmaTag, a featureless neural network architecture that jointly generates part-of-speech tags and lemmas for sentences by using bidirectional RNNs with character-level and word-level embeddings.
From POS tagging to dependency parsing for biomedical event extraction
Results: We perform an empirical study comparing state-of-the-art traditional feature-based and neural network-based models for two core natural language processing tasks of part-of-speech (POS) tagging and dependency parsing on two benchmark biomedical corpora, GENIA and CRAFT.
Semi-Supervised Sequence Modeling with Cross-View Training
tensorflow/models
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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.
Glyce: Glyph-vectors for Chinese Character Representations
ShannonAI/glyce
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NeurIPS 2019
However, due to the lack of rich pictographic evidence in glyphs and the weak generalization ability of standard computer vision models on character data, an effective way to utilize the glyph information remains to be found.
Data Augmentation via Dependency Tree Morphing for Low-Resource Languages
Neural NLP systems achieve high scores in the presence of sizable training dataset.
OmniNet: A unified architecture for multi-modal multi-task learning
subho406/OmniNet
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We also show that using this neural network pre-trained on some modalities assists in learning unseen tasks such as video captioning and video question answering.
Hierarchically-Refined Label Attention Network for Sequence Labeling
CRF has been used as a powerful model for statistical sequence labeling.
Dice Loss for Data-imbalanced NLP Tasks
ShannonAI/dice_loss_for_NLP
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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.
