TweebankNLP

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[LREC 2022] An off-the-shelf pre-trained Tweet NLP Toolkit (NER, tokenization, lemmatization, POS tagging, dependency parsing) + Tweebank-NER dataset

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# TweebankNLP
[![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
[![arXiv](https://img.shields.io/badge/arXiv-2201.07281-b31b1b.svg)](https://arxiv.org/abs/2201.07281)

This repo contains the new `Tweebank-NER` [dataset](./Tweebank-NER-v1.0) and off-the-shelf `Twitter-Stanza` pipeline for state-of-the-art Tweet NLP, as described in **[Annotating the Tweebank Corpus on Named Entity Recognition and Building NLP Models for Social Media Analysis](https://arxiv.org/abs/2201.07281)**:

- **new benchmark**: `Tweebank-NER V1.0` is the annotated NER dataset based on Tweebank V2, the main UD treebank for English Twitter NLP tasks
- **pre-trained non-transformer models**: `Twitter-Stanza` provides pre-trained Tweet NLP models with state-of-the-art on tokenization and lemmatization and competitive performance on NER, POS tagging, dependency parsing. The models are fully compatible with Stanza and provide both Python and command-line interfaces for users.  
- **pre-trained transformer models**:the state-of-the-art Twitter NER and POS tagging models are available on Hugging Face Hub: https://huggingface.co/TweebankNLP

## References

If you use this repository in your research, please kindly cite [our paper](https://arxiv.org/pdf/2201.07281.pdf) as well as the [Stanza paper](https://github.com/stanfordnlp/stanza).

```bibtex
@InProceedings{jiang-EtAl:2022:LREC2,
  author    = {Jiang, Hang  and  Hua, Yining  and  Beeferman, Doug  and  Roy, Deb},
  title     = {Annotating the Tweebank Corpus on Named Entity Recognition and Building NLP Models for Social Media Analysis},
  booktitle      = {Proceedings of the Language Resources and Evaluation Conference},
  month          = {June},
  year           = {2022},
  address        = {Marseille, France},
  publisher      = {European Language Resources Association},
  pages     = {7199--7208},
  abstract  = {Social media data such as Twitter messages ("tweets") pose a particular challenge to NLP systems because of their short, noisy, and colloquial nature. Tasks such as Named Entity Recognition (NER) and syntactic parsing require highly domain-matched training data for good performance. To date, there is no complete training corpus for both NER and syntactic analysis (e.g., part of speech tagging, dependency parsing) of tweets. While there are some publicly available annotated NLP datasets of tweets, they are only designed for individual tasks. In this study, we aim to create Tweebank-NER, an English NER corpus based on Tweebank V2 (TB2), train state-of-the-art (SOTA) Tweet NLP models on TB2, and release an NLP pipeline called Twitter-Stanza. We annotate named entities in TB2 using Amazon Mechanical Turk and measure the quality of our annotations. We train the Stanza pipeline on TB2 and compare with alternative NLP frameworks (e.g., FLAIR, spaCy) and transformer-based models. The Stanza tokenizer and lemmatizer achieve SOTA performance on TB2, while the Stanza NER tagger, part-of-speech (POS) tagger, and dependency parser achieve competitive performance against non-transformer models. The transformer-based models establish a strong baseline in Tweebank-NER and achieve the new SOTA performance in POS tagging and dependency parsing on TB2. We release the dataset and make both the Stanza pipeline and BERTweet-based models available "off-the-shelf" for use in future Tweet NLP research. Our source code, data, and pre-trained models are available at: \url{https://github.com/social-machines/TweebankNLP}.},
  url       = {https://aclanthology.org/2022.lrec-1.780}
}

@inproceedings{qi2020stanza,
    title={Stanza: A {Python} Natural Language Processing Toolkit for Many Human Languages},
    author={Qi, Peng and Zhang, Yuhao and Zhang, Yuhui and Bolton, Jason and Manning, Christopher D.},
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    year={2020}
}
```

## Installation

```
# please install from the source
pip install -e ./twitter-stanza
pip install pythainlp

# download glove and pre-trained models
sh download_twitter_resources.sh
```

## Python Interface for Twitter-Stanza
Note that Stanza allows both BIO- and BIOES-schemed annotations for training/dev/test data, but for decoding the predictions, only BIOES is used. In the evaluation process, tags are merged and thus schemes do not affect the performance scores.

```python
import stanza

# config for the `en_tweet` models (models trained only on Tweebank)
config = {
          'processors': 'tokenize,lemma,pos,depparse,ner',
          'lang': 'en',
          'tokenize_pretokenized': True, # disable tokenization
          'tokenize_model_path': './twitter-stanza/saved_models/tokenize/en_tweet_tokenizer.pt',
          'lemma_model_path': './twitter-stanza/saved_models/lemma/en_tweet_lemmatizer.pt',
          "pos_model_path": './twitter-stanza/saved_models/pos/en_tweet_tagger.pt',
          "depparse_model_path": './twitter-stanza/saved_models/depparse/en_tweet_parser.pt',
          "ner_model_path": './twitter-stanza/saved_models/ner/en_tweet_nertagger.pt',
}

# Initialize the pipeline using a configuration dict
stanza.download("en")
nlp = stanza.Pipeline(**config)
doc = nlp("Oh ikr like Messi better than Ronaldo but we all like Ronaldo more")
print(doc) # Look at the result
```

## Command-line Interface for Twitter-Stanza

### NER

We provide two pre-trained Stanza NER models:
- `en_tweetwnut17`: trained on `TB2+WNUT17`
- `en_tweet`: trained on `TB2`

NER performance comparison among spaCy, FLAIR, and Twitter-Stanza:
| Models 	| Training data 	|  NER  (F-micro)	|
|:------:	|:-------------:	|:-----:	|
|**spaCy**|      TB2      	| 52.20 	|
|        	|   TB2+WNUT17  	| 53.89 	|
|**FLAIR**|      TB2      	| 62.12 	|
|        	|   TB2+WNUT17  	| 59.08 	|
|**Stanza**|      TB2      	| 60.14 	|
|        	|   TB2+WNUT17  	|**62.53**| 	|

```
source twitter-stanza/scripts/config.sh
cd twitter-stanza

# prepare ner data
cd ./data/ner
python prepare_ner_data.py
cd ../..

# run the NER model
shorthand=en_tweetwnut17
python stanza/utils/training/run_ner.py ${shorthand} \
--mode predict \
--score_test \
--wordvec_file ./data/wordvec/English/en.twitter100d.xz \
--eval_file ./data/ner/en_tweet.test.json \
--save_dir ./saved_models/ner \
--save_name ${shorthand}_nertagger.pt \
--scheme bio
```

### Syntactic NLP Models

We provide two pre-trained models for each NLP task, please specify the following shorthand:
- `en_tweetewt`: the model trained on `TB2+UD-English-EWT`
- `en_tweet`: the model trained on `TB2`

Syntactic NLP performance comparison among spaCy, FLAIR, and Twitter-Stanza:
| Models 	| Training data 	| Tokens 	| Lemmas 	|  UPOS 	|  UAS  	|  LAS  	|
|:------:	|:-------------:	|:------:	|:------:	|:-----:	|:-----:	|:-----:	|
|**spaCy**|      TB2      	|  98.57 	|   ---  	| 86.72 	| 66.93 	| 58.79 	|
|        	|    TB2+EWT    	|  95.57 	|   ---  	| 88.84 	| 72.06 	| 63.84 	|
|**FLAIR**|      TB2      	|   ---  	|  96.18 	| 87.85 	|  ---  	|  ---  	|
|        	|    TB2+EWT    	|   ---  	|  84.54 	| 88.19 	|  ---  	|  ---  	|
|**Stanza**|      TB2      	|**98.64**|**98.65**| 93.20 	| 79.28 	| 74.34 	|
|        	|    TB2+EWT    	|  98.59 	|  85.45 	|**93.53**|**82.13**|**77.82**|

#### 1. Tokenization
```
shorthand=en_tweet
python -m stanza.utils.datasets.prepare_tokenizer_treebank ${shorthand}
python stanza/utils/training/run_tokenizer.py ${shorthand} \
--mode predict \
--score_test \
--txt_file ./data/tokenize/en_tweet.test.txt \
--label_file  ./data/tokenize/en_tweet-ud-test.toklabels \
--no_use_mwt 
```

#### 2. Lemmatization
```
shorthand=en_tweet
python -m stanza.utils.datasets.prepare_lemma_treebank ${shorthand} 
python stanza/utils/training/run_lemma.py ${shorthand} \
--mode predict \
--score_test \
--gold_file ./data/lemma/en_tweet.test.gold.conllu \
--eval_file ./data/lemma/en_tweet.test.in.conllu 
```

#### 3. POS Tagging
```
shorthand=en_tweetewt
python -m stanza.utils.datasets.prepare_pos_treebank ${shorthand} 
python stanza/utils/training/run_pos.py ${shorthand} \
--mode predict \
--score_test \
--eval_file ./data/pos/en_tweet.test.in.conllu \
--gold_file ./data/pos/en_tweet.test.gold.conllu \
--wordvec_file ./data/wordvec/English/en.twitter100d.xz \
--load_name ./saved_models/pos/${shorthand}_tagger.pt
```

#### 4. Dependency Parsing

``` 
shorthand=en_tweetewt
python -m stanza.utils.datasets.prepare_depparse_treebank ${shorthand} 
python stanza/utils/training/run_depparse.py ${shorthand} \
--mode predict \
--score_test \
--wordvec_file ./data/wordvec/English/en.twitter100d.xz \
--eval_file ./data/depparse/en_tweet.test.in.conllu \
--gold_file ./data/depparse/en_tweet.test.gold.conllu 
```

## Training Twitter-Stanza

Please refer to the [TRAIN_README.md](./TRAIN_README.md) for training the Twitter-Stanza neural pipeline.

## Acknowledgement

The Twitter-Stanza pipeline is a friendly fork from the [Stanza](https://github.com/stanfordnlp/stanza) libaray with a few modifications to adapt to tweets. The repository is fully compatible with Stanza. This research project is funded by MIT Center for Constructive Communication (CCC). This repository is mainly contributed by [Yining Hua](https://ningkko.wordpress.com/about-me/) (@ningkko) and [Hang Jiang](https://www.mit.edu/~hjian42/) (@hjian42).

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