English allows for both compounds (e.g., London-made) and phrasal paraphrases
(e.g., made in London). While these constructions have roughly the same
truth-conditional meaning, we hypothesize that the compound allows less freedom
to express the nature of the semantic relationship between the participle and
the pre-participle nominal. We thus predict that the pre-participle slot is
more constrained than the equivalent position in the phrasal construction. We
test this prediction in a large corpus by measuring the entropy of
corresponding nominal slots, conditional on the participle used. That is, we
compare the entropy of $\alpha$ in compound construction slots like
$\alpha$-[V]ed to the entropy of $\alpha$ in phrasal constructions like [V]ed
by $\alpha$ for a given verb V. As predicted, there is significantly lower
entropy in the compound construction than in the phrasal construction. We
consider how these predictions follow from more general grammatical properties
and processing factors.

通过测量语义关系的强度，对比复合结构和短语结构中的离散度，我们发现复合结构在表达分词和前分词名词之间的语义关系时比短语结构更为约束，这与我们的预测相符。此外，我们探讨了这些预测如何与语法特性和处理因素相联系。

分词前置名词熵低于分词后置名词

Participle-Prepended Nominals Have Lower Entropy Than Nominals Appended  After the Participle

This work studies the semantic representations learned by BERT for compounds,
that is, expressions such as sunlight or bodyguard. We build on recent studies
that explore semantic information in Transformers at the word level and test
whether BERT aligns with human semantic intuitions when dealing with
expressions (e.g., sunlight) whose overall meaning depends -- to a various
extent -- on the semantics of the constituent words (sun, light). We leverage a
dataset that includes human judgments on two psycholinguistic measures of
compound semantic analysis: lexeme meaning dominance (LMD; quantifying the
weight of each constituent toward the compound meaning) and semantic
transparency (ST; evaluating the extent to which the compound meaning is
recoverable from the constituents' semantics). We show that BERT-based measures
moderately align with human intuitions, especially when using contextualized
representations, and that LMD is overall more predictable than ST. Contrary to
the results reported for 'standard' words, higher, more contextualized layers
are the best at representing compound meaning. These findings shed new light on
the abilities of BERT in dealing with fine-grained semantic phenomena.
Moreover, they can provide insights into how speakers represent compounds.

研究了 BERT 对化合物的语义表征的学习，证明了 BERT 在处理 LMD 方面表现更好，并且当使用上下文化表示时，更高的层是最能代表复合词含义的。

BERT 模型中复合词的心理语言学分析

A Psycholinguistic Analysis of BERT's Representations of Compounds

The explosion of scientific publications overloads researchers with
information. This is even more dramatic for interdisciplinary studies, where
several fields need to be explored. A tool to help researchers overcome this is
Natural Language Processing (NLP): a machine-learning (ML) technique that
allows scientists to automatically synthesize information from many articles.
As a practical example, we have used NLP to conduct an interdisciplinary search
for compounds that could be carriers for Diffuse Interstellar Bands (DIBs), a
long-standing open question in astrophysics. We have trained a NLP model on a
corpus of 1.5 million cross-domain articles in open access, and fine-tuned this
model with a corpus of astrophysical publications about DIBs. Our analysis
points us toward several molecules, studied primarily in biology, having
transitions at the wavelengths of several DIBs and composed of abundant
interstellar atoms. Several of these molecules contain chromophores, small
molecular groups responsible for the molecule's colour, that could be promising
candidate carriers. Identifying viable carriers demonstrates the value of using
NLP to tackle open scientific questions, in an interdisciplinary manner.

利用自然语言处理技术和机器学习技术，从 1.5 百万个跨领域科技论文中筛选提取了多种化合物，这些可以成为漫散星际带的载体， 为解决多领域科学的开放问题提供了价值。

跨学科搜索漫散星际带的载体，利用自然语言处理

Searching for Carriers of the Diffuse Interstellar Bands Across Disciplines, using Natural Language Processing

Descriptors, which are representations of compounds, play an essential role
in machine learning of materials data. Although many representations of
elements and structures of compounds are known, these representations are
difficult to use as descriptors in their unchanged forms. This chapter shows
how compounds in a dataset can be represented as descriptors and applied to
machine-learning models for materials datasets.

该论文阐述了如何将化合物表示为描述符，并将其应用于材料数据的机器学习模型。