论文润色工具源码深度解析与开发指南

# Python示例代码：文本预处理核心类 import re import jieba import nltk from typing import List, Dict, Tuple class TextPreprocessor: def __init__(self): self.stop_words = self._load_stopwords() nltk.download('punkt') def clean_text(self, raw_text: str) -> str: """清理文本中的特殊字符和多余空格""" # 移除特殊字符但保留中英文标点 cleaned = re.sub(r'[^\w\s\u4e00-\u9fff\.\,\;\:\!\?\-\(\)]', '', raw_text) # 标准化空白字符 cleaned = re.sub(r'\s+', ' ', cleaned).strip() return cleaned def segment_text(self, text: str, language: str = 'auto') -> List[str]: """根据语言类型进行分词""" if language == 'auto': language = self._detect_language(text) if language == 'chinese': words = jieba.lcut(text) else: words = nltk.word_tokenize(text) # 过滤停用词 filtered_words = [word for word in words if word.lower() not in self.stop_words] return filtered_words def extract_features(self, text: str) -> Dict: """提取文本统计特征""" words = self.segment_text(text) sentences = nltk.sent_tokenize(text) features = { 'char_count': len(text), 'word_count': len(words), 'sentence_count': len(sentences), 'avg_word_length': sum(len(word) for word in words) / len(words) if words else 0, 'avg_sentence_length': len(words) / len(sentences) if sentences else 0 } return features

# 语法检查核心实现 import spacy from grammar_rules import AcademicGrammarRules from ml_models import GrammarClassifier class GrammarChecker: def __init__(self): self.nlp = spacy.load("en_core_web_sm") self.academic_rules = AcademicGrammarRules() self.ml_classifier = GrammarClassifier() def check_grammar(self, text: str) -> List[Dict]: """执行综合语法检查""" doc = self.nlp(text) errors = [] # 规则引擎检查 rule_errors = self.academic_rules.apply_rules(doc) errors.extend(rule_errors) # ML模型检查 ml_predictions = self.ml_classifier.predict(text) for pred in ml_predictions: if pred['confidence'] > 0.8: errors.append({ 'type': 'ml_detected', 'position': pred['position'], 'issue': pred['issue'], 'suggestion': pred['suggestion'], 'confidence': pred['confidence'] }) return self._prioritize_errors(errors) def _prioritize_errors(self, errors: List[Dict]) -> List[Dict]: """根据严重程度对错误进行排序""" severity_weights = {'critical': 3, 'major': 2, 'minor': 1} return sorted(errors, key=lambda x: (severity_weights.get(x.get('severity', 'minor'), 1), -x.get('confidence', 0)), reverse=True)

# 学术表达优化核心算法 class AcademicStyleOptimizer: def __init__(self): self.academic_lexicon = self._load_academic_lexicon() self.style_rules = self._load_style_rules() def optimize_expression(self, text: str, field: str = 'general') -> Dict: """优化学术表达""" suggestions = [] optimized_text = text # 词汇层面优化 vocab_suggestions = self._optimize_vocabulary(text, field) suggestions.extend(vocab_suggestions) # 句式结构优化 syntax_suggestions = self._optimize_syntax(text) suggestions.extend(syntax_suggestions) # 语域一致性检查 register_suggestions = self._check_register_consistency(text, field) suggestions.extend(register_suggestions) return { 'original_text': text, 'optimized_text': self._apply_suggestions(text, suggestions), 'suggestions': suggestions, 'improvement_score': self._calculate_improvement(suggestions) } def _optimize_vocabulary(self, text: str, field: str) -> List[Dict]: """优化词汇选择""" suggestions = [] words = text.split() for i, word in enumerate(words): academic_alternatives = self.academic_lexicon.get(word.lower(), []) if academic_alternatives and field in academic_alternatives: suggestions.append({ 'type': 'vocabulary_enrichment', 'position': i, 'original': word, 'suggestions': academic_alternatives[field], 'reason': f'建议使用更学术化的表达方式' }) return suggestions