aipoch/content-proofreading

When to Use

• You are preparing an academic paper for journal/conference submission and need a final language + formatting pass.
• You have bilingual (Chinese/English) content and want consistent punctuation, wording, and style across both languages.
• Your manuscript contains domain terminology (e.g., life sciences) and you need consistent Chinese–English term mapping and abbreviation rules.
• You need to validate references, numbers/units, and heading levels against a required style (APA/MLA/GB/T 7714).
• You want a shareable report (HTML or Markdown annotations) with precise error locations and revision suggestions.

Key Features

• English checks

  • Spelling (including US/UK variants)
  • Grammar (agreement, tense, articles, clause structure)
  • Punctuation conventions (US/UK)
  • Style suggestions (redundancy detection, passive voice optimization)

• Chinese checks

  • Typo/misused character detection (dictionary-based)
  • Grammar and collocation checks
  • Chinese vs. English punctuation normalization
  • Academic expression optimization suggestions

• Terminology consistency

  • Domain terminology database (life sciences by default)
  • Bidirectional Chinese–English correspondence checks
  • Abbreviation rules (require full form on first occurrence)
  • Synonym unification to preferred standard terms

• Formatting checks

  • Reference style validation (APA/MLA/GB/T 7714, etc.)
  • Number and unit normalization
  • Heading level consistency
  • Abbreviation consistency across the document

• Reporting

  • HTML interactive report or Markdown annotations
  • Precise error localization
  • Actionable revision suggestions

Dependencies

• Python: >= 3.8

• Python packages (install via pip install -r requirements.txt)

  • languagetool-python (version: see requirements.txt) — English grammar checking
  • opencc (version: see requirements.txt) — Traditional/Simplified Chinese conversion
  • jieba (version: see requirements.txt) — Chinese tokenization
  • pyenchant (version: see requirements.txt) — spelling checks
  • markdown (version: see requirements.txt) — Markdown rendering
  • python-docx (version: see requirements.txt) — .docx reading
  • docx2pdf (version: see requirements.txt) — Word-to-PDF conversion

Example Usage

1) Install

python -m venv .venv
source .venv/bin/activate  # Windows: .venv\Scripts\activate

pip install -r requirements.txt

2) Run (basic)

python scripts/init_run.py --input <paper_file_path> --output <output_path>

3) Run (advanced)

python scripts/init_run.py \
  --input paper.md \
  --output report.html \
  --lang en \
  --style apa \
  --terminology biology \
  --format html

4) CLI parameters

| Parameter | Description | Default | |---|---|---| | --input | Input file path | Required | | --output | Output report path | Generates an HTML report by default | | --lang | Language to check (en / zh / both) | both | | --style | Reference style (apa / mla / gb) | apa | | --terminology | Domain terminology set | biology | | --format | Output format (html / markdown) | html | | --no-pdf | Skip PDF generation during Word→PDF conversion | false |

5) Use as a Python module (end-to-end)

from scripts.english_checker import EnglishChecker
from scripts.chinese_checker import ChineseChecker
from scripts.terminology_manager import TerminologyManager
from scripts.annotation_generator import AnnotationGenerator

text = """
Messenger RNA (mRNA) is transcribed in the nucleus.
"""

en_checker = EnglishChecker()
zh_checker = ChineseChecker()
term_manager = TerminologyManager(domain="biology")

results = []
results.extend(en_checker.check(text))
results.extend(zh_checker.check(text))
results.extend(term_manager.check(text))

generator = AnnotationGenerator(output_format="html")
report = generator.generate(results)

with open("report.html", "w", encoding="utf-8") as f:
    f.write(report)

Implementation Details

Architecture / Core Modules

• english_checker.py

  • Core engine for English spelling/grammar/style checks.
  • Designed to be rule-extensible (add or register new rule sets).

• chinese_checker.py

  • Core engine for Chinese typo/grammar/style checks.
  • Includes a library of common academic writing error patterns.

• terminology_manager.py

  • Terminology database management (import/export/query/update).
  • Performs term consistency checks, bilingual mapping validation, and abbreviation policy checks.

• annotation_generator.py

  • Converts detected issues into a visual report (HTML) or annotated Markdown.
  • Ensures issues include location, type, and suggested fix.

• word_converter.py

  • Extracts text from .docx.
  • Optionally converts Word to PDF (can be disabled via --no-pdf).

Terminology database format (JSON)

Organized by domain; each entry can include bilingual forms and abbreviation metadata:

{
  "biology": {
    "cell": {
      "en": "cell",
      "abbrev": null,
      "full_form": null
    },
    "mrna": {
      "en": "mRNA",
      "abbrev": "mRNA",
      "full_form": "messenger RNA"
    }
  }
}

Checking logic (typical):

• If an abbreviation (e.g., mRNA) appears, verify the full form appears at first mention (e.g., messenger RNA (mRNA)).
• If both Chinese and English terms appear, verify they match the configured mapping for the selected domain.
• If synonyms are detected, prefer the standardized term defined in the database.

Rule database format (JSON)

Rules are grouped by language and category:

{
  "english": {
    "spelling": [],
    "grammar": [],
    "style": []
  },
  "format": {
    "references": [],
    "numbers": [],
    "units": []
  }
}

How rules are applied (high level):

• Load rule sets by --lang and --style.
• Run language-specific checks (English/Chinese) and formatting checks.
• Merge results into a unified issue list.
• Render issues into the selected output format (html / markdown) with location-aware annotations.

Extensibility

• Add new rules

  1. Create a rule file under assets/rules/.
  2. Implement rules following the project’s rule template.
  3. Register the rule set in the rule index.
  4. Run tests to validate precision/recall and avoid false positives.

• Add new terminology sets

  1. Create a terminology JSON under assets/terminology/.
  2. Follow the domain structure shown above.
  3. Register the new domain in the terminology index so it can be selected via --terminology.