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Autori principali: Liu, Zhu, Hu, Zhen, Dai, Lei, Xuan, Yu, Liu, Ying
Natura: Preprint
Pubblicazione: 2025
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Accesso online:https://arxiv.org/abs/2507.04070
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author Liu, Zhu
Hu, Zhen
Dai, Lei
Xuan, Yu
Liu, Ying
author_facet Liu, Zhu
Hu, Zhen
Dai, Lei
Xuan, Yu
Liu, Ying
contents Semantic map models visualize systematic relations among semantic functions through graph structures and are widely used in linguistic typology. However, existing construction methods either depend on labor-intensive expert reasoning or on fully automated systems lacking expert involvement, creating a tension between scalability and interpretability. We introduce \textbf{XISM}, an interactive system that combines data-driven inference with expert knowledge. XISM generates candidate maps via a top-down procedure and allows users to iteratively refine edges in a visual interface, with real-time metric feedback. Experiments in three semantic domains and expert interviews show that XISM improves linguistic decision transparency and controllability in semantic-map construction while maintaining computational efficiency. XISM provides a collaborative approach for scalable and interpretable semantic-map building. The system\footnote{https://app.xism2025.xin/} , source code\footnote{https://github.com/hank317/XISM} , and demonstration video\footnote{https://youtu.be/m5laLhGn6Ys} are publicly available.
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publishDate 2025
record_format arxiv
spellingShingle XISM: an eXploratory and Interactive Graph Tool to Visualize and Evaluate Semantic Map Models
Liu, Zhu
Hu, Zhen
Dai, Lei
Xuan, Yu
Liu, Ying
Computation and Language
Semantic map models visualize systematic relations among semantic functions through graph structures and are widely used in linguistic typology. However, existing construction methods either depend on labor-intensive expert reasoning or on fully automated systems lacking expert involvement, creating a tension between scalability and interpretability. We introduce \textbf{XISM}, an interactive system that combines data-driven inference with expert knowledge. XISM generates candidate maps via a top-down procedure and allows users to iteratively refine edges in a visual interface, with real-time metric feedback. Experiments in three semantic domains and expert interviews show that XISM improves linguistic decision transparency and controllability in semantic-map construction while maintaining computational efficiency. XISM provides a collaborative approach for scalable and interpretable semantic-map building. The system\footnote{https://app.xism2025.xin/} , source code\footnote{https://github.com/hank317/XISM} , and demonstration video\footnote{https://youtu.be/m5laLhGn6Ys} are publicly available.
title XISM: an eXploratory and Interactive Graph Tool to Visualize and Evaluate Semantic Map Models
topic Computation and Language
url https://arxiv.org/abs/2507.04070