Knowledge in the human mind exhibits a dualistic vector/network nature.
Modelling words as vectors is key to natural language processing, whereas
networks of word associations can map the nature of semantic memory. We
reconcile these paradigms - fragmented across linguistics, psychology and
computer science - by introducing FEature-Rich MUltiplex LEXical (FERMULEX)
networks. This novel framework merges structural similarities in networks and
vector features of words, which can be combined or explored independently.
Similarities model heterogenous word associations across
semantic/syntactic/phonological aspects of knowledge. Words are enriched with
multi-dimensional feature embeddings including frequency, age of acquisition,
length and polysemy. These aspects enable unprecedented explorations of
cognitive knowledge. Through CHILDES data, we use FERMULEX networks to model
normative language acquisition by 1000 toddlers between 18 and 30 months.
Similarities and embeddings capture word homophily via conformity, which
measures assortative mixing via distance and features. Conformity unearths a
language kernel of frequent/polysemous/short nouns and verbs key for basic
sentence production, supporting recent evidence of children's syntactic
constructs emerging at 30 months. This kernel is invisible to network
core-detection and feature-only clustering: It emerges from the dual
vector/network nature of words. Our quantitative analysis reveals two key
strategies in early word learning. Modelling word acquisition as random walks
on FERMULEX topology, we highlight non-uniform filling of communicative
developmental inventories (CDIs). Conformity-based walkers lead to accurate
(75%), precise (55%) and partially well-recalled (34%) predictions of early
word learning in CDIs, providing quantitative support to previous empirical
findings and developmental theories.

本研究提出一种新的 FERMULEX 结构，通过多维度特征嵌入来丰富词义。我们运用该网络结构，研究了 1000 位 18-30 个月大的幼儿语言习得中的词汇选择偏好和语法建构，并发现其非均匀和规律性的演化过程。研究结果支持了以往的实证研究和发展性理论。