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.

Lexical evolution is universal across languages, yet the mechanisms driving neologism formation vary substantially from one language to another. In Chinese, this variation is particularly reflected in the productivity of compounding, where morphemes are combined through word-formation relations to create interpretable lexical forms. This morphological productivity implies that the Chinese lexicon can be viewed not only as a set of attested words, but also as a dynamically latent space with potential neologisms to be realized. However, this perspective still remains underexplored in computation, as current approaches are largely reactive, focusing on detecting attested words from corpora. This raises a question about word-formation itself: which morpheme combinations do the rules license beyond those that corpora already attest? Motivated by this, we propose a proactive creation paradigm for Chinese neologism research. We introduce a three-stage pipeline: KG-Based Candidate Generation, Morphological Discarding, and LLM-Based Filtering. We then probe the candidates with three complementary signals: expert judgment, model perplexity, and corpus attestation. The three signals agree: accepted candidates sit close to real words and clearly apart from rejected combinations. The pipeline yields 18,662 candidates with definitions, examples, and formation metadata, released as material for follow-up work, such as lexical gaps and lexical change. Overall, this study substantiates a proactive creation paradigm for Chinese neologism research, and uses it to characterize what Chinese word-formation rules license beyond what dictionaries have already recorded.

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.