Colexification refers to the linguistic phenomenon where a single lexical
form is used to convey multiple meanings. By studying cross-lingual
colexifications, researchers have gained valuable insights into fields such as
psycholinguistics and cognitive sciences [Jackson et al.,2019]. While several
multilingual colexification datasets exist, there is untapped potential in
using this information to bootstrap datasets across such semantic features. In
this paper, we aim to demonstrate how colexifications can be leveraged to
create such cross-lingual datasets. We showcase curation procedures which
result in a dataset covering 142 languages across 21 language families across
the world. The dataset includes ratings of concreteness and affectiveness,
mapped with phonemes and phonological features. We further analyze the dataset
along different dimensions to demonstrate potential of the proposed procedures
in facilitating further interdisciplinary research in psychology, cognitive
science, and multilingual natural language processing (NLP). Based on initial
investigations, we observe that i) colexifications that are closer in
concreteness/affectiveness are more likely to colexify; ii) certain
initial/last phonemes are significantly correlated with
concreteness/affectiveness intra language families, such as /k/ as the initial
phoneme in both Turkic and Tai-Kadai correlated with concreteness, and /p/ in
Dravidian and Sino-Tibetan correlated with Valence; iii) the type-to-token
ratio (TTR) of phonemes are positively correlated with concreteness across
several language families, while the length of phoneme segments are negatively
correlated with concreteness; iv) certain phonological features are negatively
correlated with concreteness across languages. The dataset is made public
online for further research.

本文介绍了如何通过共分化现象的利用，建立跨语言数据集，以分析其在精神语言学、认知科学和多语言自然语言处理中的潜力，并在研究中发现了属于不同语言家族的元音和音素的显着相似性。

跨语言数据集引导的共现词性：以音系、抽象性和情感为案例

Colexifications for Bootstrapping Cross-lingual Datasets: The Case of  Phonology, Concreteness, and Affectiveness

Entity alignment (EA) aims at building a unified Knowledge Graph (KG) of rich
content by linking the equivalent entities from various KGs. GNN-based EA
methods present promising performances by modeling the KG structure defined by
relation triples. However, attribute triples can also provide crucial alignment
signal but have not been well explored yet. In this paper, we propose to
utilize an attributed value encoder and partition the KG into subgraphs to
model the various types of attribute triples efficiently. Besides, the
performances of current EA methods are overestimated because of the name-bias
of existing EA datasets. To make an objective evaluation, we propose a hard
experimental setting where we select equivalent entity pairs with very
different names as the test set. Under both the regular and hard settings, our
method achieves significant improvements ($5.10\%$ on average Hits@$1$ in
DBP$15$k) over $12$ baselines in cross-lingual and monolingual datasets.
Ablation studies on different subgraphs and a case study about attribute types
further demonstrate the effectiveness of our method. Source code and data can
be found at this https URL

本文利用属性值编码器将知识图谱分为子图，以有效地建模各种类型的属性三元组，提出一种能够显著提高在跨语言和单语数据集下各项指标的 Entity Alignment 方法，并在不同子图上进行深入的消融研究和实证案例探究.