HiTZ/Multilingual-Opinion-Target-Extraction

---
arxiv: 2210.12623
paperswithcode_id: aspect-based-sentiment-analysis
license: apache-2.0
configs:
- config_name: en
data_files:
- split: train
path: en.ote.train.json
- split: test
path: en.ote.test.json
- config_name: es
data_files:
- split: train
path: es.ote.train.json
- split: test
path: es.ote.test.json
- config_name: fr
data_files:
- split: train
path: fr.ote.train.json
- split: test
path: fr.ote.test.json
- config_name: ru
data_files:
- split: train
path: ru.ote.train.json
- split: test
path: ru.ote.test.json
- config_name: tr
data_files:
- split: train
path: tr.ote.train.json
task_categories:
- token-classification
language:
- en
- fr
- es
- ru
- tr
tags:
- opinion
- target
- absa
- aspect
- sentiment analysis
pretty_name: Multilingual Opinion Target Extraction
size_categories:
- 1K<n<10K

---

This repository contains the English '[SemEval-2014 Task 4: Aspect Based Sentiment Analysis](https://aclanthology.org/S14-2004/)'. translated with DeepL into Spanish, French, Russian, and Turkish. The **labels have been manually projected**. For more details, read this paper: [Model and Data Transfer for Cross-Lingual Sequence Labelling in Zero-Resource Settings](https://arxiv.org/abs/2210.12623).

**Intended Usage**: Since the datasets are parallel across languages, they are ideal for evaluating annotation projection algorithms, such as [T-Projection](https://arxiv.org/abs/2212.10548).

# Label Dictionary

```python
{
"O": 0,
"B-TARGET": 1,
"I-TARGET": 2
}
```

# Cication

If you use this data, please cite the following papers:

```bibtex
@inproceedings{garcia-ferrero-etal-2022-model,
title = "Model and Data Transfer for Cross-Lingual Sequence Labelling in Zero-Resource Settings",
author = "Garc{\'\i}a-Ferrero, Iker and
Agerri, Rodrigo and
Rigau, German",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.478",
doi = "10.18653/v1/2022.findings-emnlp.478",
pages = "6403--6416",
abstract = "Zero-resource cross-lingual transfer approaches aim to apply supervised modelsfrom a source language to unlabelled target languages. In this paper we performan in-depth study of the two main techniques employed so far for cross-lingualzero-resource sequence labelling, based either on data or model transfer. Although previous research has proposed translation and annotation projection(data-based cross-lingual transfer) as an effective technique for cross-lingualsequence labelling, in this paper we experimentally demonstrate that highcapacity multilingual language models applied in a zero-shot (model-basedcross-lingual transfer) setting consistently outperform data-basedcross-lingual transfer approaches. A detailed analysis of our results suggeststhat this might be due to important differences in language use. Morespecifically, machine translation often generates a textual signal which isdifferent to what the models are exposed to when using gold standard data,which affects both the fine-tuning and evaluation processes. Our results alsoindicate that data-based cross-lingual transfer approaches remain a competitiveoption when high-capacity multilingual language models are not available.",
}

@inproceedings{pontiki-etal-2014-semeval,
title = "{S}em{E}val-2014 Task 4: Aspect Based Sentiment Analysis",
author = "Pontiki, Maria and
Galanis, Dimitris and
Pavlopoulos, John and
Papageorgiou, Harris and
Androutsopoulos, Ion and
Manandhar, Suresh",
editor = "Nakov, Preslav and
Zesch, Torsten",
booktitle = "Proceedings of the 8th International Workshop on Semantic Evaluation ({S}em{E}val 2014)",
month = aug,
year = "2014",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S14-2004",
doi = "10.3115/v1/S14-2004",
pages = "27--35",
}
```

原始信息汇总

数据集概述

基本信息

• arxiv: 2210.12623
• paperswithcode_id: aspect-based-sentiment-analysis
• license: apache-2.0
• pretty_name: Multilingual Opinion Target Extraction
• size_categories: 1K<n<10K
• task_categories: token-classification
• language: en, fr, es, ru, tr
• tags: opinion, target, absa, aspect, sentiment analysis

数据文件配置

• config_name: en
  • train: en.ote.train.json
  • test: en.ote.test.json
• config_name: es
  • train: es.ote.train.json
  • test: es.ote.test.json
• config_name: fr
  • train: fr.ote.train.json
  • test: fr.ote.test.json
• config_name: ru
  • train: ru.ote.train.json
  • test: ru.ote.test.json
• config_name: tr
  • train: tr.ote.train.json

标签字典

python { "O": 0, "B-TARGET": 1, "I-TARGET": 2 }