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 |
A standard dataset for POS tagging is the Wall Street Journal (WSJ) portion of the Penn Treebank, containing 45 different POS tags. Sections 0-18 are used for training, sections 19-21 for development, and sections 22-24 for testing. Models are evaluated based on accuracy.
| Model | Accuracy | Paper / Source |
|---|---|---|
| Meta BiLSTM (Bohnet et al., 2018) | 97.96 | Morphosyntactic Tagging with a Meta-BiLSTM Model over Context Sensitive Tokenn Encodings |
| Char Bi-LSTM (Ling et al., 2015) | 97.78 | Finding Function in Form: Compositional Character Models for Open Vocabulary Word Representation |
| Adversarial Bi-LSTM (Yasunaga et al., 2018) | 97.59 | Robust Multilingual Part-of-Speech Tagging via Adversarial Training |
| Yang et al. (2017) | 97.55 | Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networks |
| Ma and Hovy (2016) | 97.55 | End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF |
| Feed Forward (Vaswani et a. 2016) | 97.4 | Supertagging with LSTMs |
| Bi-LSTM (Ling et al., 2017) | 97.36 | Finding Function in Form: Compositional Character Models for Open Vocabulary Word Representation |
| Bi-LSTM (Plank et al., 2016) | 97.22 | Multilingual Part-of-Speech Tagging with Bidirectional Long Short-Term Memory Models and Auxiliary Loss |
The Ritter (2011) dataset has become the benchmark for social media part-of-speech tagging. This is comprised of some 50K tokens of English social media sampled in late 2011, and is tagged using an extended version of the PTB tagset.
| Model | Accuracy | Paper |
|---|---|---|
| GATE | 88.69 | Twitter Part-of-Speech Tagging for All: Overcoming Sparse and Noisy Data |
| CMU | 90.0 ± 0.5 | Improved Part-of-Speech Tagging for Online Conversational Text with Word Clusters |
Universal Dependencies (UD) is a framework for cross-linguistic grammatical annotation, which contains more than 100 treebanks in over 60 languages. Models are typically evaluated based on the average test accuracy across 28 languages.
| Model | Avg accuracy | Paper / Source |
|---|---|---|
| Adversarial Bi-LSTM (Yasunaga et al., 2018) | 96.73 | Robust Multilingual Part-of-Speech Tagging via Adversarial Training |
| Bi-LSTM (Plank et al., 2016) | 96.40 | Multilingual Part-of-Speech Tagging with Bidirectional Long Short-Term Memory Models and Auxiliary Loss |
| Joint Bi-LSTM (Nguyen et al., 2017) | 95.55 | A Novel Neural Network Model for Joint POS Tagging and Graph-based Dependency Parsing |