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--- |
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license: apache-2.0 |
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datasets: |
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- ltg/norec |
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language: |
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- 'no' |
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pipeline_tag: token-classification |
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model-index: |
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- name: SSA-Perin |
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results: |
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- task: |
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type: structured sentiment analysis |
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dataset: |
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name: NoReC |
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type: NoReC |
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metrics: |
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- name: Unlabeled sentiment tuple F1 |
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type: Unlabeled sentiment tuple F1 |
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value: 44.12% |
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- name: Target F1 |
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type: Target F1 |
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value: 56.44% |
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- name: Relative polarity precision |
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type: Relative polarity precision |
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value: 93.19% |
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--- |
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This repository contains a pretrained model (and an easy-to-run wrapper for it) for structured sentiment analysis in Norwegian language, pre-trained on the [NoReC_fine dataset](https://github.com/ltgoslo/norec_fine). |
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This is an implementation of the method described in |
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```bibtex |
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@misc{samuel2022direct, |
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title={Direct parsing to sentiment graphs}, |
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author={David Samuel and Jeremy Barnes and Robin Kurtz and Stephan Oepen and Lilja Øvrelid and Erik Velldal}, |
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year={2022}, |
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eprint={2203.13209}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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The main repository that also contains the scripts for training the model, can be found on the project [github](https://github.com/jerbarnes/direct_parsing_to_sent_graph). |
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The model is also available in the form of a [HF space](https://huggingface.co/spaces/ltg/ssa-perin). |
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The sentiment graph model is based on an underlying masked language model – [NorBERT 2](https://huggingface.co/ltg/norbert2). |
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The proposed method suggests three different ways to encode the sentiment graph: "node-centric", "labeled-edge", and "opinion-tuple". |
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The current model |
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- uses "labeled-edge" graph encoding |
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- does not use character-level embedding |
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- all other hyperparameters are set to [default values](https://github.com/jerbarnes/direct_parsing_to_sent_graph/blob/main/perin/config/edge_norec.yaml) |
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, and it achieves the following results on the held-out set of the dataset: |
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| Unlabeled sentiment tuple F1 | Target F1 | Relative polarity precision | |
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|:----------------------------:|:----------:|:---------------------------:| |
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| 0.434 | 0.541 | 0.926 | |
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The model can be easily used for predicting sentiment tuples as follows: |
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```python |
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>>> import model_wrapper |
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>>> model = model_wrapper.PredictionModel() |
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>>> model.predict(['vi liker svart kaffe']) |
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[{'sent_id': '0', |
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'text': 'vi liker svart kaffe', |
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'opinions': [{'Source': [['vi'], ['0:2']], |
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'Target': [['svart', 'kaffe'], ['9:14', '15:20']], |
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'Polar_expression': [['liker'], ['3:8']], |
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'Polarity': 'Positive'}]}] |
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``` |
