This paper provides a comprehensive overview of the gapping dataset for
Russian that consists of 7.5k sentences with gapping (as well as 15k relevant
negative sentences) and comprises data from various genres: news, fiction,
social media and technical texts. The dataset was prepared for the Automatic
Gapping Resolution Shared Task for Russian (AGRR-2019) - a competition aimed at
stimulating the development of NLP tools and methods for processing of
ellipsis.
In this paper, we pay special attention to the gapping resolution methods
that were introduced within the shared task as well as an alternative test set
that illustrates that our corpus is a diverse and representative subset of
Russian language gapping sufficient for effective utilization of machine
learning techniques.

本文提供了一个包含 7.5k 个俄语 gapping 句子的全面数据集，旨在促进 NLP 工具和方法的发展，特别关注于共享任务中引入的 gapping 解析方法以及代表性的测试集，以便有效地利用机器学习技术。

AGRR-2019: 俄语间断语句的语言语料库

AGRR-2019: A Corpus for Gapping Resolution in Russian

Sentences with gapping, such as Paul likes coffee and Mary tea, lack an overt
predicate to indicate the relation between two or more arguments. Surface
syntax representations of such sentences are often produced poorly by parsers,
and even if correct, not well suited to downstream natural language
understanding tasks such as relation extraction that are typically designed to
extract information from sentences with canonical clause structure. In this
paper, we present two methods for parsing to a Universal Dependencies graph
representation that explicitly encodes the elided material with additional
nodes and edges. We find that both methods can reconstruct elided material from
dependency trees with high accuracy when the parser correctly predicts the
existence of a gap. We further demonstrate that one of our methods can be
applied to other languages based on a case study on Swedish.

本文提出两种方法将省略部分显式地编码到附加结点和边中，以解决包含省略部分的句子的表面句法表示质量较差和下游自然语言理解任务中的信息提取难题，其中一种方法适用于其他语言，如瑞典语。