This paper presents the methodology and data used for the automatic
extraction of the Romanian Academic Word List (Ro-AWL). Academic Word Lists are
useful in both L2 and L1 teaching contexts. For the Romanian language, no such
resource exists so far. Ro-AWL has been generated by combining methods from
corpus and computational linguistics with L2 academic writing approaches. We
use two types of data: (a) existing data, such as the Romanian Frequency List
based on the ROMBAC corpus, and (b) self-compiled data, such as the expert
academic writing corpus EXPRES. For constructing the academic word list, we
follow the methodology for building the Academic Vocabulary List for the
English language. The distribution of Ro-AWL features (general distribution,
POS distribution) into four disciplinary datasets is in line with previous
research. Ro-AWL is freely available and can be used for teaching, research and
NLP applications.

本研究介绍了生成罗马尼亚学术词汇表（Ro-AWL）的方法和数据，Ro-AWL 结合语料库、计算语言学和二语学术写作方法生成，可用于罗马尼亚语的教学、研究和自然语言处理应用。

罗马尼亚学术词汇表的自动提取：数据和方法

Automatic Extraction of the Romanian Academic Word List: Data and  Methods

The idea that discourse relations are construed through explicit content and
shared, or implicit, knowledge between producer and interpreter is ubiquitous
in discourse research and linguistics. However, the actual contribution of the
lexical semantics of arguments is unclear. We propose a computational approach
to the analysis of contrast and concession relations in the PDTB corpus. Our
work sheds light on the extent to which lexical semantics contributes to
signaling explicit and implicit discourse relations and clarifies the
contribution of different parts of speech in both. This study contributes to
bridging the gap between corpus linguistics and computational linguistics by
proposing transparent and explainable models of discourse relations based on
the synonymy and antonymy of their arguments.

该研究提出了一种基于语义标记的计算方法来分析语篇中的异议和让步关系，澄清了语言学中的词汇语义在引发语篇中显性和隐性关系方面的贡献，并探讨了不同词性在其中所扮演的角色，从而为语料库和计算语言学之间的研究架起了一座桥梁。