SGER: Beyond the Core: A Pilot Project on Cataloguing Grammatical Constructions and Multiword Expressions in English.

大多数以词为中心的文本语言注释都是通过识别关键词并标记周围的短语来进行的，这些短语显示了它们在关键词所唤起的意义结构中的作用。这个程序忽略了大多数习语（变得更糟）和不规则的语法模式（只有这样她才会同意）。“超越核心”项目正在探索用多词单位的分层表示和这些文本中的“非核心”语法结构来增强这些注释的方法。为了实现这一目标，研究人员使用FrameNet注释工具在文本中寻找非核心结构，并以一种显示它们如何满足单个结构所规定的形式和语义约束的方式标记短语。这些信息被存档的“构筑物”将每个构筑物与举例说明的注释句子联系起来。尽管计算语言学社区对非核心结构有浓厚的兴趣，但研究人员不知道有多少非核心结构，它们在NLP应用中的重要性，它们在不同类型的文本中的频率，或者使训练有素的语言学家能够识别它们的技能是否能够可靠地传达给时间紧迫的注释者。这一实证研究提供了缺失的信息。Constructicon和完整的注释将通过FrameNet网站提供给研究人员，以人类可浏览和机器可读的形式。这些数据将为解析、语言理解和组合语义的研究提供丰富的材料，并可能作为机器学习方法的训练语料库，用于检测原始文本中已知的非核心结构。

Most word-centered linguistic annotations of texts proceed by identifying keywords and labeling the phrases around them that show their roles in the meaning structures evoked by the keywords. This procedure misses most idioms (took a turn for the worse) and irregular grammatical patterns (only then would she agree to it). The "Beyond the Core" project is exploring ways of augmenting such annotations with layered representations of multiword units and "non-core" grammatical constructions present in such texts. Toward this end, using FrameNet annotation tools, researchers are finding non-core structures in texts and labeling the phrases in a way that shows how they satisfy formal and semantic constraints dictated by the individual constructions. The "Constructicon", where such information is archived, links each construction with annotated sentences that exemplify it.Although there is a strong interest in non-core structures in the Computational Linguistics community, researchers don't know how many there are, how important they are in NLP applications, how frequent they are in texts of different kinds, or whether the skills that enable trained linguists to recognize them can be reliably communicated to time-pressured annotators. This empirical study is providing that missing information.The Constructicon and the full body of annotations will be made available to researchers via the FrameNet website, in both human-browsable and machine-readable form. The data will provide rich material for research on parsing, language understanding, and compositional semantics, and may possibly serve as a training corpus for machine-learning methods of detecting known non-core constructions in raw text.