We present a syntactic dependency treebank for naturalistic child and
child-directed speech in English (MacWhinney, 2000). Our annotations largely
followed the guidelines of the Universal Dependencies project (UD (Zeman et
al., 2022)), with detailed extensions to lexical/syntactic structures unique to
conversational speech (in opposition to written texts). Compared to existing
UD-style spoken treebanks as well as other dependency corpora of child-parent
interactions specifically, our dataset is of (much) larger size (N of
utterances = 44,744; N of words = 233, 907) and contains speech from a total of
10 children covering a wide age range (18-66 months). With this dataset, we
ask: (1) How well would state-of-the-art dependency parsers, tailored for the
written domain, perform for speech of different interlocutors in spontaneous
conversations? (2) What is the relationship between parser performance and the
developmental stage of the child? To address these questions, in ongoing work,
we are conducting thorough dependency parser evaluations using both graph-based
and transition-based parsers with different hyperparameterization, trained from
three different types of out-of-domain written texts: news, tweets, and learner
data.

本研究提出了一个语法依存树库，用于儿童自然语言的注释和研究，分析了写作和口语之间的语言特征并探讨了儿童语言发展与句法分析器性能的关系。

基于数据的儿童 - 家长互动解析评估

Data-driven Parsing Evaluation for Child-Parent Interactions

Dependency parsers are among the most crucial tools in natural language
processing as they have many important applications in downstream tasks such as
information retrieval, machine translation and knowledge acquisition. We
introduce the Yara Parser, a fast and accurate open-source dependency parser
based on the arc-eager algorithm and beam search. It achieves an unlabeled
accuracy of 93.32 on the standard WSJ test set which ranks it among the top
dependency parsers. At its fastest, Yara can parse about 4000 sentences per
second when in greedy mode (1 beam). When optimizing for accuracy (using 64
beams and Brown cluster features), Yara can parse 45 sentences per second. The
parser can be trained on any syntactic dependency treebank and different
options are provided in order to make it more flexible and tunable for specific
tasks. It is released with the Apache version 2.0 license and can be used for
both commercial and academic purposes. The parser can be found at
this https URL

介绍了 Yara Parser，它是一种基于 arc-eager 算法和 beam 搜索的快速准确的开源依赖分析器，在自然语言处理中有许多重要的应用，具有可扩展性和可调节性。