Argumentation sentences 1.0
type: Dataset
distribution: Landing page for the dataset
description: I. IDENTIFYING INFORMATION Title* Argumentation sentences Subtitle A translated corpus for classifying sentence stance in relation to a topic. Created by* Anna Lindahl (anna.lindahl@svenska.gu.se) Publisher(s)* Språkbanken Text (sb-info@svenska.gu.se) Link(s) / permanent identifier(s)* https://spraakbanken.gu.se/en/resources/superlim License(s)* CC BY 4.0 Abstract* Argumentation sentences is a translated corpus for the task of identifying stance in relation to a topic. It consists of sentences labeled with pro, con or non in relation to one of six topics. The original dataset [1] can be found here https://github.com/trtm/AURC. The test set is manually corrected translations, the training set is machine translated. Funded by* Vinnova (grant no. 2021-04165) Cite as Related datasets Part of the SuperLim collection (https://spraakbanken.gu.se/en/resources/superlim) II. USAGE Key applications Machine learning, argumentation mining, stance classification Intended task(s)/usage(s) Evaluate models on the following task: Given a sentence and a topic, determine if the sentence is for, against or neutral in relation to the topic. Recommended evaluation measures Krippendorff’s alpha (the official SuperLim measure), MCC, F Dataset function(s) Training, testing Recommended split(s) Train, dev, test (provided) III. DATA Primary data* Text Language* Swedish Dataset in numbers* 5265 sentences split over 6 topics, 3450 train, 750 dev and 1065 test Nature of the content* Topics: Abortion, Death penalty, Nuclear power, Marijuana legalization, Minimum wage, Cloning. Each topic has a set of associated sentences, lableled with pro, con or non in relation to the topic. Format* Jsonl with the following keys: sentence_id = the id for each sentence, topic = the topic for each sentence, label = the label for each sentence, can be pro, con or non, sentence = the sentence itself Tab-separated with 4 columns: the id for each sentence, topic = the topic for each sentence, label = the label for eac…
modified: 2023-03-30T00:00:00Z
issued: 2024-01-01T00:00:00Z
publisher: University of Gothenburg
qualified attribution: Attribution
  had role: principalInvestigator
  agent: Lindahl, Anna
    type: Agent
    type: PrivateIndividual(s)
keyword: Language Technology (Computational Linguistics)
keyword: corpus
language: SWE

Landing page for the dataset
type: Distribution
title: Landningssida för datasetet @sv
access URL: Argumentation sentences 1.0
availability: stable

University of Gothenburg
type: Agent
name: Göteborgs universitet @sv
type: Academia-ScientificOrganisation
source: 01tm6cn81