6 papers
Clusters are All You Need: Pre-Training the Tsetlin Machine with Semantic Clusters from Language Models for Interpretability
Jiechao Gao, Rohan Kumar Yadav, Yuangang Li +4
Pre-trained language models such as BERT achieve strong text classification performance but lack transparency, limiting their use in high-stakes settings. The Tsetlin Machine (TM)…
LLM-Guided Semantic Bootstrapping for Interpretable Text Classification with Tsetlin Machines
Jiechao Gao, Rohan Kumar Yadav, Yuangang Li +4
Pretrained language models (PLMs) like BERT provide strong semantic representations but are costly and opaque, while symbolic models such as the Tsetlin Machine (TM) offer transpar…
XISM: an eXploratory and Interactive Graph Tool to Visualize and Evaluate Semantic Map Models
Zhu Liu, Zhen Hu, Lei Dai +2
Semantic map models visualize systematic relations among semantic functions through graph structures and are widely used in linguistic typology. However, existing construction meth…
From the New World of Word Embeddings: A Comparative Study of Small-World Lexico-Semantic Networks in LLMs
Zhu Liu, Ying Liu, KangYang Luo +2
Lexico-semantic networks represent words as nodes and their semantic relatedness as edges. While such networks are traditionally constructed using embeddings from encoder-based mod…
A Top-down Graph-based Tool for Modeling Classical Semantic Maps: A Crosslinguistic Case Study of Supplementary Adverbs
Zhu Liu, Cunliang Kong, Ying Liu +1
Semantic map models (SMMs) construct a network-like conceptual space from cross-linguistic instances or forms, based on the connectivity hypothesis. This approach has been widely u…
JuniperLiu at CoMeDi Shared Task: Models as Annotators in Lexical Semantics Disagreements
Zhu Liu, Zhen Hu, Ying Liu
We present the results of our system for the CoMeDi Shared Task, which predicts majority votes (Subtask 1) and annotator disagreements (Subtask 2). Our approach combines model ense…