This paper presents a merged collection of entity and coreference annotated
data grounded in the Universal Dependencies (UD) treebanks for the two written
forms of Norwegian: Bokm{\aa}l and Nynorsk. The aligned and converted corpora
are the \textit{Norwegian Named Entities} (NorNE) and \textit{Norwegian
Anaphora Resolution Corpus} (NARC). While NorNE is aligned with an older
version of the treebank, NARC is misaligned and requires extensive
transformation from the original annotations to the UD structure and CoNLL-U
format. We here demonstrate the conversion and alignment processes, along with
an analysis of discovered issues and errors in the data -- some of which
include data split overlaps in the original treebank. These procedures and the
developed system may prove helpful for future corpus alignment and coreference
annotation endeavors. The merged corpora comprise the first Norwegian UD
treebank enriched with named entities and coreference information.

本文提出了一个整合了个体和共指注释数据的合并集合，该集合基于挪威两种书写形式中的通用依赖 (UD) 树库，即 Bokmål 和 Nynorsk。该合并后的数据集提供了首个挪威 UD 树库，其中包含命名实体和共指信息。

将挪威 UD 树库与实体和指代信息对齐

Aligning the Norwegian UD Treebank with Entity and Coreference  Information

In this work, we focus on low-resource dependency parsing for multiple
languages. Several strategies are tailored to enhance performance in
low-resource scenarios. While these are well-known to the community, it is not
trivial to select the best-performing combination of these strategies for a
low-resource language that we are interested in, and not much attention has
been given to measuring the efficacy of these strategies. We experiment with 5
low-resource strategies for our ensembled approach on 7 Universal Dependency
(UD) low-resource languages. Our exhaustive experimentation on these languages
supports the effective improvements for languages not covered in pretrained
models. We show a successful application of the ensembled system on a truly
low-resource language Sanskrit. The code and data are available at:
this https URL

研究多语言的低资源依存句法分析，提出了 5 种针对低资源场景的策略并对其进行了实验，发现这些策略可以有效提高在预训练模型中没包含语言的依存句法分析精度，最终成功运用在梵文等低资源语言中。

针对低资源依存句法分析定制的策略系统研究

Systematic Investigation of Strategies Tailored for Low-Resource Settings for Low-Resource Dependency Parsing

We propose a new method for projective dependency parsing based on headed
spans. In a projective dependency tree, the largest subtree rooted at each word
covers a contiguous sequence (i.e., a span) in the surface order. We call such
a span marked by a root word \textit{headed span}.
A projective dependency tree can be represented as a collection of headed
spans. We decompose the score of a dependency tree into the scores of the
headed spans and design a novel $O(n^3)$ dynamic programming algorithm to
enable global training and exact inference. Our model achieves state-of-the-art
or competitive results on PTB, CTB, and UD. Our code is publicly available at
https://github.com/sustcsonglin/span-based-dependency-parsing.

我们提出了一种新的基于 headed spans 的投影依存解析方法。我们将从每个单词开始的最大子树表示为一组 headed spans，将一个依存树的得分分解为所有 headed spans 的得分， 并设计了一个新颖的 O (n3) 动态编程算法实现全局训练和准确推理。我们的方法在 PTB，CTB 和 UD 上实现了最先进或有竞争力的结果。