arXiv cs.CL· Hari Sitaula, Manish Sakhakarmy, Pujan Pant, Sagar Newpane·· 14 小时前AI 评分18
将 LLM-SC 适配到活态神圣遗产：Pashupatinath 神庙评论的领域特定抽取、语义网络与人工评估
Adapting LLM-SC to Living Sacred Heritage: Domain-Specific Extraction, Semantic Networks and Human Evaluation on Pashupatinath Temple Reviews
AI 导读
研究将 LLM-SC 框架适配到尼泊尔 Pashupatinath 神庙，在 GPT-4o 抽取提示词中定义 12 类实体（4 类原始 LLM-SC 类型加 8 类神圣遗产类型）、4 种关系和 9 个方面-情感维度，处理 2,500 条英文 Tripadvisor 评论。
正文 · 原文
Abstract:Large language model (LLM) pipelines can turn visitor reviews into structured data, but generic schemes may miss rituals, sacred spaces, cultural restrictions and emotionally mixed experiences at living sacred heritage sites. We adapted the LLM-Social Computing (LLM-SC) framework to Pashupatinath Temple, Nepal, by specifying a 12-type entity taxonomy (four original LLM-SC types plus eight sacred-heritage types), four relation types and nine aspect-sentiment dimensions in a GPT-4o extraction prompt, which was applied to a fixed random sample of 2,500 English-language Tripadvisor reviews from a processed corpus of 3,939. The exports contain 11,286 entity, 6,232 aspect-sentiment and 5,590 relation records, covering 2,485, 2,487 and 2,433 reviews, respectively; the eight added sacred-heritage types account for 64.8% of entity records. Against an adjudicated two-annotator reference for 200 reviews, exact normalized surface-and-type matching gave provisional entity scores of 41.0% precision, 21.8% recall and 28.4% micro-F1. Aspect-presence F1 was 53.6% and end-to-end aspect-plus-polarity F1 was 42.6%. Human adjudication supported 153 of 194 first-listed relation candidates (one per review; 78.9%), a candidate-level rate that does not estimate relation recall or whole-graph precision. The contribution is a culturally grounded, domain-specific adaptation with component-wise human evaluation and explicit reproducibility and generalization limitations.
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.35685 [cs.CL] |
| (or arXiv:2610.35685v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.35685 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Hari Sitaula [view email]
[v1]
Mon, 26 Oct 2026 16:20:00 UTC (605 KB)
来源：arXiv cs.CL · arxiv.org