论文标题
检测多字表达类型有助于词汇复杂性评估
Detecting Multiword Expression Type Helps Lexical Complexity Assessment
论文作者
论文摘要
多字表达式（MWES）代表词汇，由于其特质性质，应将其视为单个词汇单元。已显示多个NLP应用可以从MWE识别中受益，但是关于MWES词汇复杂性的研究仍然是一个探索的区域。在这项工作中，我们重新注释了Yimam等人的复杂单词标识共享任务2018数据集。 （2017年），为一系列Lexemes提供复杂性分数，并具有MWES类型。我们使用本文发布了MWE通知的数据集，我们相信该数据集代表了文本简化社区的宝贵资源。此外，我们研究了哪种类型的表达方式对于天然和非本地读者最有问题。最后，我们表明词汇复杂性评估系统受益于有关MWE类型的信息。
Multiword expressions (MWEs) represent lexemes that should be treated as single lexical units due to their idiosyncratic nature. Multiple NLP applications have been shown to benefit from MWE identification, however the research on lexical complexity of MWEs is still an under-explored area. In this work, we re-annotate the Complex Word Identification Shared Task 2018 dataset of Yimam et al. (2017), which provides complexity scores for a range of lexemes, with the types of MWEs. We release the MWE-annotated dataset with this paper, and we believe this dataset represents a valuable resource for the text simplification community. In addition, we investigate which types of expressions are most problematic for native and non-native readers. Finally, we show that a lexical complexity assessment system benefits from the information about MWE types.
