FRACAS: A FRench Annotated Corpus of Attribution relations in newS
- URL: http://arxiv.org/abs/2309.10604v1
- Date: Tue, 19 Sep 2023 13:19:54 GMT
- Title: FRACAS: A FRench Annotated Corpus of Attribution relations in newS
- Authors: Ange Richard, Laura Alonzo-Canul, Fran\c{c}ois Portet
- Abstract summary: We present a manually annotated corpus of 1676 newswire texts in French for quotation extraction and source attribution.
We first describe the composition of our corpus and the choices that were made in selecting the data.
We then detail our inter-annotator agreement between the 8 annotators who worked on manual labelling.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Quotation extraction is a widely useful task both from a sociological and
from a Natural Language Processing perspective. However, very little data is
available to study this task in languages other than English. In this paper, we
present a manually annotated corpus of 1676 newswire texts in French for
quotation extraction and source attribution. We first describe the composition
of our corpus and the choices that were made in selecting the data. We then
detail the annotation guidelines and annotation process, as well as a few
statistics about the final corpus and the obtained balance between quote types
(direct, indirect and mixed, which are particularly challenging). We end by
detailing our inter-annotator agreement between the 8 annotators who worked on
manual labelling, which is substantially high for such a difficult linguistic
phenomenon.
Related papers
- FASSILA: A Corpus for Algerian Dialect Fake News Detection and Sentiment Analysis [0.0]
The Algerian dialect (AD) faces challenges due to the absence of annotated corpora.
This study outlines the development process of a specialized corpus for Fake News (FN) detection and sentiment analysis (SA) in AD called FASSILA.
arXiv Detail & Related papers (2024-11-07T10:39:10Z) - IDEAL: Influence-Driven Selective Annotations Empower In-Context
Learners in Large Language Models [66.32043210237768]
This paper introduces an influence-driven selective annotation method.
It aims to minimize annotation costs while improving the quality of in-context examples.
Experiments confirm the superiority of the proposed method on various benchmarks.
arXiv Detail & Related papers (2023-10-16T22:53:54Z) - A Corpus for Sentence-level Subjectivity Detection on English News Articles [49.49218203204942]
We use our guidelines to collect NewsSD-ENG, a corpus of 638 objective and 411 subjective sentences extracted from English news articles on controversial topics.
Our corpus paves the way for subjectivity detection in English without relying on language-specific tools, such as lexicons or machine translation.
arXiv Detail & Related papers (2023-05-29T11:54:50Z) - Beyond Contrastive Learning: A Variational Generative Model for
Multilingual Retrieval [109.62363167257664]
We propose a generative model for learning multilingual text embeddings.
Our model operates on parallel data in $N$ languages.
We evaluate this method on a suite of tasks including semantic similarity, bitext mining, and cross-lingual question retrieval.
arXiv Detail & Related papers (2022-12-21T02:41:40Z) - Quotations, Coreference Resolution, and Sentiment Annotations in
Croatian News Articles: An Exploratory Study [0.0]
The paper focuses on the annotation of the quotation, co-reference resolution, and sentiment annotation in SETimes news corpus in Croatian.
The generated corpus with quotation features annotations can be used for multiple tasks in the field of Natural Language Processing.
arXiv Detail & Related papers (2022-12-14T11:54:12Z) - Models and Datasets for Cross-Lingual Summarisation [78.56238251185214]
We present a cross-lingual summarisation corpus with long documents in a source language associated with multi-sentence summaries in a target language.
The corpus covers twelve language pairs and directions for four European languages, namely Czech, English, French and German.
We derive cross-lingual document-summary instances from Wikipedia by combining lead paragraphs and articles' bodies from language aligned Wikipedia titles.
arXiv Detail & Related papers (2022-02-19T11:55:40Z) - WikiLingua: A New Benchmark Dataset for Cross-Lingual Abstractive
Summarization [41.578594261746055]
We introduce WikiLingua, a large-scale, multilingual dataset for the evaluation of crosslingual abstractive summarization systems.
We extract article and summary pairs in 18 languages from WikiHow, a high quality, collaborative resource of how-to guides on a diverse set of topics written by human authors.
We create gold-standard article-summary alignments across languages by aligning the images that are used to describe each how-to step in an article.
arXiv Detail & Related papers (2020-10-07T00:28:05Z) - Automatic Extraction of Rules Governing Morphological Agreement [103.78033184221373]
We develop an automated framework for extracting a first-pass grammatical specification from raw text.
We focus on extracting rules describing agreement, a morphosyntactic phenomenon at the core of the grammars of many of the world's languages.
We apply our framework to all languages included in the Universal Dependencies project, with promising results.
arXiv Detail & Related papers (2020-10-02T18:31:45Z) - The Discussion Tracker Corpus of Collaborative Argumentation [2.800857580710507]
The Discussion Tracker corpus was collected in American high school English classes.
The corpus consists of 29 multi-party discussions of English literature transcribed from 985 minutes of audio.
arXiv Detail & Related papers (2020-05-22T18:27:28Z) - On the Language Neutrality of Pre-trained Multilingual Representations [70.93503607755055]
We investigate the language-neutrality of multilingual contextual embeddings directly and with respect to lexical semantics.
Our results show that contextual embeddings are more language-neutral and, in general, more informative than aligned static word-type embeddings.
We show how to reach state-of-the-art accuracy on language identification and match the performance of statistical methods for word alignment of parallel sentences.
arXiv Detail & Related papers (2020-04-09T19:50:32Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.