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.

本文介绍了一个手动注释的法语新闻语料库，用于引述提取和来源归属。语料库详细描述了数据选择和注释指南，统计了引述类型的平衡情况，并展示了参与手动标注的 8 名注释员之间的高的注释者一致性。

FRACAS: 一份用于新闻中归属关系的法语标注语料库

FRACAS: A FRench Annotated Corpus of Attribution relations in newS

Quotation extraction aims to extract quotations from written text. There are
three components in a quotation: source refers to the holder of the quotation,
cue is the trigger word(s), and content is the main body. Existing solutions
for quotation extraction mainly utilize rule-based approaches and sequence
labeling models. While rule-based approaches often lead to low recalls,
sequence labeling models cannot well handle quotations with complicated
structures. In this paper, we propose the Context and Former-Label Enhanced Net
(CofeNet) for quotation extraction. CofeNet is able to extract complicated
quotations with components of variable lengths and complicated structures. On
two public datasets (i.e., PolNeAR and Riqua) and one proprietary dataset
(i.e., PoliticsZH), we show that our CofeNet achieves state-of-the-art
performance on complicated quotation extraction.

本文提出了上下文和先前标签增强网络 (CofeNet)，针对文本中具有不同长度和复杂结构的引语进行提取，相比于基于规则和序列标注模型的方法，CofeNet 能够在 PolNeAR、Riqua 和 PoliticsZH 数据集上取得最先进的性能。