metadata

language:
  - 'no'
  - nb
  - nn
license: cc-by-nc-4.0
size_categories:
  - 10K<n<100K
task_categories:
  - token-classification
pretty_name: NoReC TSA
dataset_info:
  features:
    - name: idx
      dtype: string
    - name: tokens
      sequence: string
    - name: tsa_tags
      sequence: string
  splits:
    - name: train
      num_bytes: 2296476
      num_examples: 8634
    - name: validation
      num_bytes: 411562
      num_examples: 1531
    - name: test
      num_bytes: 346288
      num_examples: 1272
  download_size: 899078
  dataset_size: 3054326
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
      - split: test
        path: data/test-*
  - config_name: intensity
    data_files:
      - split: train
        path: intensity/train-*
      - split: validation
        path: intensity/validation-*
      - split: test
        path: intensity/test-*

Dataset Card for NoReC TSA

Dataset Description

Repository: https://github.com/ltgoslo/norec_tsa
Paper: A Fine-Grained Sentiment Dataset for Norwegian

Dataset Summary

The dataset contains tokenized Norwegian sentences where each token is tagged for sentiment expressed towards that token. The dataset is derived from the manually annotated NoReC_fine with rich annotations for each sentiment expression in the texts.
The texts are a subset of the Norewegian Review Corpus NoReC.

Supported Tasks and Leaderboards

NorBench provides TSA evaluation scripts using this dataset, and a leaderboard comparing large language models for downstream NLP tasks in Norwegian.

Languages

Norwegian: Predominantly Bokmål written variant.

variant	split	sents	docs
nb	dev	1531	44
nb	test	1272	47
nb	train	8556	323
nn	train	78	4

Dataset Structure

The dataset comes in two flavours:

default configuration yields labels with binary Positive / Negative sentiment description
intensity configuration yields labels with additional sentiment intensity, 1: Slight, 2: Standard, and 3: Strong.

The config is required for accessing the version with intensity. tsa_data = load_dataset("ltg/norec_tsa", "intensity") The dataset comes with predefined train, dev (vallidation) and test splits.

Data Instances

Config "default" example instance:

{'idx': '701363-08-02',
 'tokens': ['Vi', 'liker', 'det', '.'],
 'tsa_tags': ['O', 'O', 'B-targ-Positive', 'O']}

Config "intensity" example instance:

{'idx': '701363-08-02',
 'tokens': ['Vi', 'liker', 'det', '.'],
 'tsa_tags': ['O', 'O', 'B-targ-Positive-2', 'O']}

Data Fields

idx(str): Unique document-and sentence identifier from NoReC_fine. The 6-digit document identifier can also be used to look up the text and its metadata in NoReC.
tokens: (List[str]): List of the tokens in the sentence
tsa_tags: (List[str]): List of the tags for each token in BIO format. There is no integer representation of these in the dataset.

Data Splits

DatasetDict({
    test: Dataset({
        features: ['idx', 'tokens', 'tsa_tags'],
        num_rows: 1272
    })
    train: Dataset({
        features: ['idx', 'tokens', 'tsa_tags'],
        num_rows: 8634
    })
    validation: Dataset({
        features: ['idx', 'tokens', 'tsa_tags'],
        num_rows: 1531
    })
})

Dataset Creation

Curation Rationale

The sentiment expressions and targets are annotated in NoReC_fine according to its annotation guidelines

Since a sentiment target may be the target of several sentiment expressions, these are resolved to a final sentiment polarity (and intensity) using the conversion script in NoReC_tsa. There is no "mixed" sentiment category. When a target is the receiver of both positive and negative sentiment, the strongest wins. If a tie, the last sentiment wins.

Source Data

A subset of the Norwegian Review Corpus with its sources and preprocessing described here.

Discussion of Biases

The professional review texts in NoReC that NoReC_tsa is a subset from, are from a set number of Norwegian Publishing channels and from a set timespan that can be explored in the NoReC metadata. Both language usage and sentiments expressed could have been more diverse with a more diverse set of source texts.

Licensing Information

The data, being derived from NoReC, is distributed under a Creative Commons Attribution-NonCommercial licence (CC BY-NC 4.0), access the full license text here: https://creativecommons.org/licenses/by-nc/4.0/

The licence is motivated by the need to block the possibility of third parties redistributing the orignal reviews for commercial purposes. Note that machine learned models, extracted lexicons, embeddings, and similar resources that are created on the basis of NoReC are not considered to contain the original data and so can be freely used also for commercial purposes despite the non-commercial condition.

Citation Information

@InProceedings{OvrMaeBar20,   
author = {Lilja Øvrelid and Petter Mæhlum and Jeremy Barnes and Erik Velldal},   
title = {A Fine-grained Sentiment Dataset for {N}orwegian}, 
booktitle = {{Proceedings of the 12th Edition of the Language Resources and Evaluation Conference}},  
year = 2020,  
address = {Marseille, France, 2020} 
}