Proper noun compounds, e.g., "Covid vaccine", convey information in a
succinct manner (a "Covid vaccine" is a "vaccine that immunizes against the
Covid disease"). These are commonly used in short-form domains, such as news
headlines, but are largely ignored in information-seeking applications. To
address this limitation, we release a new manually annotated dataset, ProNCI,
consisting of 22.5K proper noun compounds along with their free-form semantic
interpretations. ProNCI is 60 times larger than prior noun compound datasets
and also includes non-compositional examples, which have not been previously
explored. We experiment with various neural models for automatically generating
the semantic interpretations from proper noun compounds, ranging from few-shot
prompting to supervised learning, with varying degrees of knowledge about the
constituent nouns. We find that adding targeted knowledge, particularly about
the common noun, results in performance gains of upto 2.8%. Finally, we
integrate our model generated interpretations with an existing Open IE system
and observe an 7.5% increase in yield at a precision of 85%. The dataset and
code are available at this https URL.

通过各种神经模型，尤其是针对基本名词的共同知识，我们可以自动生成专有名词复合词的语义解释。将这些解释与现有的 Open IE 系统集成，可以获得更高的收益率和更高的准确性。

新冠疫苗可预防 Covid，牛津疫苗则是在牛津制造的！—— 关于专有名词复合词的语义解释

"Covid vaccine is against Covid but Oxford vaccine is made at Oxford!" Semantic Interpretation of Proper Noun Compounds

Information Extraction (IE) from scientific texts can be used to guide
readers to the central information in scientific documents. But narrow IE
systems extract only a fraction of the information captured, and Open IE
systems do not perform well on the long and complex sentences encountered in
scientific texts. In this work we combine the output of both types of systems
to achieve Semi-Open Relation Extraction, a new task that we explore in the
Biology domain. First, we present the Focused Open Biological Information
Extraction (FOBIE) dataset and use FOBIE to train a state-of-the-art narrow
scientific IE system to extract trade-off relations and arguments that are
central to biology texts. We then run both the narrow IE system and a
state-of-the-art Open IE system on a corpus of 10k open-access scientific
biological texts. We show that a significant amount (65%) of erroneous and
uninformative Open IE extractions can be filtered using narrow IE extractions.
Furthermore, we show that the retained extractions are significantly more often
informative to a reader.

本研究针对科技文本的开放式关系抽取技术不足问题，将窄 IE 系统和开放式 IE 系统结合，提出了一种新的任务 —— 半开放式关系抽取，并应用于生物领域。研究在 FOBIE 数据集上，训练出能够提取生物学文本中重要关系和论述的最先进的窄 IE 系统，并在 10k 篇生物学开放式科技文本上运行窄 IE 系统和开放式 IE 系统，取得了过滤出 65% 的错误和无用开放式 IE 提取结果，并且发现保留下来的提取结果对于读者更有信息价值。

半开放的科技文本中关系提取初探

In Layman's Terms: Semi-Open Relation Extraction from Scientific Texts

We introduce Graphene, an Open IE system whose goal is to generate accurate,
meaningful and complete propositions that may facilitate a variety of
downstream semantic applications. For this purpose, we transform syntactically
complex input sentences into clean, compact structures in the form of core
facts and accompanying contexts, while identifying the rhetorical relations
that hold between them in order to maintain their semantic relationship. In
that way, we preserve the context of the relational tuples extracted from a
source sentence, generating a novel lightweight semantic representation for
Open IE that enhances the expressiveness of the extracted propositions.

Graphene 是一种基于 Open IE 系统的轻量级的语义表述，能够生成准确、有意义和完整的命题，并维护其上下文和各种语言关系，从而提高所提取命题的表现力。