A Reproducible Protocol for Constructing a Chinese–English Ceramic Cultural Tourism Lexicon using Registered Corpora

Summary

This protocol constructs a Chinese–English ceramic cultural tourism lexicon using registered bilingual corpora, standardized cleaning, candidate term extraction, similarity-ranked alignment, and expert validation with adjudication. Using this workflow, 60 verified entries were exported with definitions, usage examples, provenance records, and TBX-compatible outputs.

Abstract

Building bilingual terminology resources for ceramic cultural tourism is challenging because relevant texts are often fragmented, translation choices are inconsistent, and reusable construction workflows are rarely documented. This protocol presents a reproducible, step-by-step workflow for constructing a Chinese–English ceramic cultural tourism lexicon through corpus registration and cleaning, candidate term extraction, bilingual alignment, and expert validation with adjudication. Applied to a registered bilingual corpus, the workflow generated shortlisted Chinese and English candidate terms, produced ranked bilingual term pairs, and retained validated alignments after independent expert review. The finalized lexicon exported 60 verified entries with bilingual term forms, domain tags, translation types, bilingual definitions, usage examples, provenance records, and interoperable outputs such as Excel and TBX files. To support transparency and rerunability, the protocol also records thresholds, package versions, frozen resources, and validation decisions throughout the workflow. This makes the procedure auditable and easier to adapt to other heritage tourism domains. When transferred to a new domain, users can extend the domain keyword list, update the bilingual mapping resource, and adjust a small number of shortlist and similarity thresholds according to corpus size and terminology density.

Introduction

Cultural tourism has become an important driver of destination development as travelers increasingly seek heritage-based experiences that are meaningful rather than purely recreational¹. At the policy and industry levels, international organizations have repeatedly emphasized the value of linking tourism with culture and improving interpretation, documentation, and communication across diverse heritage assets. Within this broader context, ceramic cultural tourism is especially terminology-dense because visitors regularly encounter specialized concepts related to materials, glaze and firing processes, kiln technologies, stylistic motifs, artifac....

Protocol

This study used only publicly available or institution-authorized textual data and did not involve human or animal subjects. No institutional ethical approval was required.

1. Create the project workspace

1. Create a project folder and the following subfolders: corpus_raw, corpus_clean, candidates, alignment, validation, outputs, logs, and resources.
2. Create the run log and record the environment settings.
   1. Create logs/run_notes.txt.
   2. Record the run date/time, operating system version, and project scope as “Chinese–English ceramic cultural tourism terminology”.
   ....

Results

Corpus assembly and cleaning yield
Following the corpus design shown in Table 1, a registered bilingual corpus was assembled from museum and exhibition texts, heritage descriptions, and visitor-facing tourism materials. After cleaning, the corpus comprised 12 Chinese documents and 8 English documents, totaling 47,843 Chinese characters and 32,193 English characters³⁰. The corpus retained the intended combination of technical, interpretive, and tourism-oriented.......

Discussion

The main value of this protocol lies in its ability to transform a terminology task that is often handled informally into a reproducible and reviewable workflow. In ceramic cultural tourism, many terms are both technical and interpretive: they refer not only to materials, kiln types, firing stages, or decorative styles, but also to how these concepts are communicated to visitors in labels, narratives, and educational materials³⁶. For that reason, bilingual variation in this domain is not a minor s.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Computer workstation	Hardware	Any modern computer capable of running Python 3.11 and processing text corpora; ≥8 GB RAM recommended	No catalog number required Source: Generic (any supplier)
Operating system	Software	Windows 10/11, macOS, or Linux (record exact OS version in logs/run_notes.txt)	Version recorded in run_notes.txt Source: System-installed
Python	Software	Python 3.11	Version recorded in logs/pip_freeze.txt Source: Python Software Foundation distribution
jieba	Software library	0.42.1	jieba==0.42.1 Source: PyPI package repository
spaCy	Software library	3.7.2	spacy==3.7.2 Source: PyPI package repository
English pipeline model	NLP model	en_core_web_sm 3.7.1 (spaCy model package)	en_core_web_sm==3.7.1; installation recorded in run_notes.txt Source: spaCy model repository
scikit-learn	Software library	1.4.2	scikit-learn==1.4.2 Source: PyPI package repository
RapidFuzz	Software library	3.6.1	RapidFuzz==3.6.1 Source: PyPI package repository
Spreadsheet editor	Software	Any software capable of editing .xlsx files	Used to view/edit SourceRegister.xlsx, CorpusStats.xlsx, Candidates/Shortlist files Source: Generic (any supplier)
Plain-text editor	Software	Any software capable of editing .txt and .csv files	Used to view/edit logs/.txt and resources/.csv Source: Generic (any supplier)
UTF-8 text conversion tool	Software	Any tool capable of exporting documents to UTF-8 plain text	Used in Step 2.3; exact method recorded in run_notes.txt Source: Generic (any supplier)
SourceRegister template	File template	SourceRegister.xlsx with fields: source_id, title, owner/publisher, language, genre, access_date, license_note	Frozen as outputs/SourceRegister_v1.xlsx Source: Created in this study
Corpus statistics template	File template	CorpusStats.xlsx with fields: source_id, lang, n_chars_before, n_chars_after, timestamp, cleaning_ruleset	Generated during Step 3.6 Source: Created in this study
Chinese user dictionary	Resource file	resources/userdict_zh.txt (domain-specific term list for segmentation support)	Frozen copy saved; checksum recorded in thresholds.txt Source: Created/curated in this study
Domain keyword map	Resource file	resources/domain_keywords.csv with domain_tag priority rules	Frozen as outputs/domain_keywords_v1.csv; checksum recorded in alignment_log.txt Source: Created/curated in this study
Chinese-to-English term mapping table	Resource file	resources/term_map_zh2en.xlsx with columns term_zh and term_zh_en	Frozen as outputs/term_map_zh2en_v1.xlsx; coverage recorded in alignment_log.txt Source: Created/curated in this study
Threshold log	Log file	logs/thresholds.txt (patterns, thresholds, library versions, resource checksums)	Required for deterministic reruns Source: Generated in this study
Alignment log	Log file	logs/alignment_log.txt (weights, k, cutoffs, vectorizer settings, mapping coverage, map checksums)	Required for deterministic reruns Source: Generated in this study
Annotation sheet	File template	validation/Annotation.xlsx (pair_id, term_zh, term_en, S, decisions, adjudication, rationale)	Frozen as outputs/Annotation_v1.xlsx after Step 6.6 Source: Created in this study
Evaluation output	Output file	outputs/Evaluation.xlsx (raw agreement, Cohen’s κ, totals)	Produced in Step 6.4 Source: Generated in this study
Final lexicon export	Output file	outputs/FinalLexicon.xlsx following Table 2 schema	Produced in Step 7.2 Source: Generated in this study
TBX export	Output file	TBX file generated from FinalLexicon.xlsx with stable entry_id mapping	Validation result recorded in run_notes.txt Source: Generated in this study