We aim to provide an explanation for how the human brain might connect words
for sentence formation. A novel approach to modeling syntactic representation
is introduced, potentially showing the existence of universal syntactic
structures for all natural languages. As the discovery of DNA's double helix
structure shed light on the inner workings of genetics, we wish to introduce a
basic understanding of how language might work in the human brain. It could be
the brain's way of encoding and decoding knowledge. It also brings some insight
into theories in linguistics, psychology, and cognitive science. After looking
into the logic behind universal syntactic structures and the methodology of the
modeling technique, we attempt to analyze corpora that showcase universality in
the language process of different natural languages such as English and Korean.
Lastly, we discuss the critical period hypothesis, universal grammar, and a few
other assertions on language for the purpose of advancing our understanding of
the human brain.

我们旨在解释人脑如何将单词连接起来形成句子，通过引入一种新颖的模型来建模句法表示，可能显示出所有自然语言的普遍句法结构的存在，从而提供人类大脑中语言工作方式的基本理解，同时为语言学、心理学和认知科学的理论提供一些见解。我们研究了普遍句法结构的逻辑和建模技术的方法论，试图分析展示不同自然语言（如英语和韩语）中语言过程的普遍性的语料库。最后，我们讨论了关于人类大脑理解方面的关键周期假说、普遍语法以及其他一些语言断言。

通用句法结构：为多种自然语言建模句法

Universal Syntactic Structures: Modeling Syntax for Various Natural  Languages

This paper analyses the degree to which dialect classifiers based on
syntactic representations remain stable over space and time. While previous
work has shown that the combination of grammar induction and geospatial text
classification produces robust dialect models, we do not know what influence
both changing grammars and changing populations have on dialect models. This
paper constructs a test set for 12 dialects of English that spans three years
at monthly intervals with a fixed spatial distribution across 1,120 cities.
Syntactic representations are formulated within the usage-based Construction
Grammar paradigm (CxG). The decay rate of classification performance for each
dialect over time allows us to identify regions undergoing syntactic change.
And the distribution of classification accuracy within dialect regions allows
us to identify the degree to which the grammar of a dialect is internally
heterogeneous. The main contribution of this paper is to show that a rigorous
evaluation of dialect classification models can be used to find both variation
over space and change over time.

本文分析了基于句法表示的方言分类器在空间和时间上的稳定程度，并构建了一个测试集，评估了 12 种英语方言在 3 年内随时间变化的分类准确性和语法变化速率。通过在语用建构语法范式（CxG）中制定的句法表示，可以识别在时间和空间上发生语法变化的地区。本文的主要贡献在于表明严格评估方言分类模型可用于发现空间上的变异和时间上的变化。

句法方言分类的时空稳定性

Stability of Syntactic Dialect Classification Over Space and Time

We advance the state of the art in biomolecular interaction extraction with
three contributions: (i) We show that deep, Abstract Meaning Representations
(AMR) significantly improve the accuracy of a biomolecular interaction
extraction system when compared to a baseline that relies solely on surface-
and syntax-based features; (ii) In contrast with previous approaches that infer
relations on a sentence-by-sentence basis, we expand our framework to enable
consistent predictions over sets of sentences (documents); (iii) We further
modify and expand a graph kernel learning framework to enable concurrent
exploitation of automatically induced AMR (semantic) and dependency structure
(syntactic) representations. Our experiments show that our approach yields
interaction extraction systems that are more robust in environments where there
is a significant mismatch between training and test conditions.

本文研究使用抽象意义表示 (AMR)、依存句法和图核学习框架的结合，提高了生物分子相互作用提取系统的准确性和鲁棒性。

利用医学文本的语义解析提取生物分子相互作用

Extracting Biomolecular Interactions Using Semantic Parsing of  Biomedical Text

In this work, we address the problem to model all the nodes (words or
phrases) in a dependency tree with the dense representations. We propose a
recursive convolutional neural network (RCNN) architecture to capture syntactic
and compositional-semantic representations of phrases and words in a dependency
tree. Different with the original recursive neural network, we introduce the
convolution and pooling layers, which can model a variety of compositions by
the feature maps and choose the most informative compositions by the pooling
layers. Based on RCNN, we use a discriminative model to re-rank a $k$-best list
of candidate dependency parsing trees. The experiments show that RCNN is very
effective to improve the state-of-the-art dependency parsing on both English
and Chinese datasets.

该研究提出了一种新的递归卷积神经网络（RCNN）体系结构，用于在依存树中建模节点，并在依存分析结果上取得了很好的效果。