Are compounds and phrases constructed by the same combinatorial principles? This is a long-standing topic in linguistic theory. As a highly isolating language, Chinese offers a particularly revealing case: its word–phrase boundary is weakly marked, thus compound-internal relations often parallel phrase-level syntactic relations. Although this continuity is well described in Chinese linguistics, no operational test has yet examined whether the same principles are similarly represented across the two levels. PLMs provide a shared representational space for operationalizing this question. Motivated by this idea, we build CompPhrase-7, a parallel Chinese compound–phrase dataset covering seven shared relation types, and conduct a multi-dimensional probing study of Chinese-language PLMs, which separates three levels of cross-scale continuity: transferable feature information, aligned category geometry, and localized dimension-level causal support. The results show a clear dissociation: feature transfer is stable across models; category geometry depends strongly on pre-training regime; and localized causal support is not detected. The findings point to a differentiated view of Chinese combinatorial principles: compounds and phrases retain comparable relational cues, while lexicalization reshapes how those cues are organized once phrase-like structures become words. This study connects a long-standing linguistic question to a more precise computational representation, showing that continuity can surface as transferable information without shared geometry or localized causal implementation.

Lexical Relation Mining (LRM) aims to identify and classify lexical relations between word pairs. In this paper, we focus on two subtypes of LRM: Lexical Relation Classification (LRC) and Lexical Entailment (LE). Existing top-performing methods for them rely heavily on Pre-trained Language Models (PLMs) yet fail to distinguish nuanced lexical relations. From a linguistic perspective, intralexical tree-structured sememe information can reflect interlexical relations. Inspired by this, we are motivated to explore leveraging such structured knowledge to enhance LRC and LE. We first propose an automated Sememe Tree Construction (STC) pipeline to predict sememe trees; Then, we present the SememeLRM method to fully leverage structured sememe knowledge; Experimental results show that it achieves a notable 1.6% improvement on average across benchmarks, even outperforming Large Language Model (LLM)-based methods that contain 20 times more parameters on most benchmarks. Further results also suggest that sememe trees predicted by our pipeline can rival the gold-standard in HowNet, extending their applicability to lexico-semantic computing. Overall, this paper presents a potentially generalizable framework for leveraging complete sememe trees and makes significant progress, helping to unlock the value of such intralexical knowledge in downstream tasks.

Link Prediction (LP) aims to predict missing triple information within a Knowledge Graph (KG). Existing LP methods have sought to improve the performance by integrating structural and textual information. However, for lexico-semantic KGs designed to document fine-grained sense distinctions, these types of information may not be sufficient to support effective LP. From a linguistic perspective, word senses within lexico-semantic relations usually show systematic differences in their sememic components. In light of this, we are motivated to enhance LP with sememe knowledge. We first construct a Sememe Prediction (SP) dataset, SememeDef, for learning such knowledge, and two Chinese datasets, HN7 and CWN5, for LP evaluation; Then, we propose a method, SememeLP, to leverage this knowledge for LP fully. It consistently and significantly improves the LP performance in both English and Chinese, achieving SOTA MRR of 75.1%, 80.5%, and 77.1% on WN18RR, HN7, and CWN5, respectively; Finally, an in-depth analysis is conducted, making clear how sememic components can benefit LP for lexico-semantic KGs, which provides promising progress for the completion of them.