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"""
Text normalization utilities for comparing bibliography entries.
"""
import re
import unicodedata
from unidecode import unidecode
class TextNormalizer:
"""Utility class for normalizing text for comparison."""
# LaTeX command patterns
LATEX_COMMANDS = [
(r'\\textbf\{([^}]*)\}', r'\1'),
(r'\\textit\{([^}]*)\}', r'\1'),
(r'\\emph\{([^}]*)\}', r'\1'),
(r'\\textrm\{([^}]*)\}', r'\1'),
(r'\\texttt\{([^}]*)\}', r'\1'),
(r'\\textsf\{([^}]*)\}', r'\1'),
(r'\\textsc\{([^}]*)\}', r'\1'),
(r'\\text\{([^}]*)\}', r'\1'),
(r'\\mathrm\{([^}]*)\}', r'\1'),
(r'\\mathbf\{([^}]*)\}', r'\1'),
(r'\\mathit\{([^}]*)\}', r'\1'),
(r'\\url\{([^}]*)\}', r'\1'),
(r'\\href\{[^}]*\}\{([^}]*)\}', r'\1'),
]
# LaTeX special character mappings
LATEX_CHARS = {
r'\&': '&',
r'\%': '%',
r'\$': '$',
r'\#': '#',
r'\_': '_',
r'\{': '{',
r'\}': '}',
r'\~': '~',
r'\^': '^',
r'``': '"',
r"''": '"',
r'`': "'",
r"'": "'",
r'--': '–',
r'---': '—',
}
# LaTeX accent commands
LATEX_ACCENTS = [
(r"\\'([aeiouAEIOU])", r'\1'), # acute
(r'\\`([aeiouAEIOU])', r'\1'), # grave
(r'\\^([aeiouAEIOU])', r'\1'), # circumflex
(r'\\"([aeiouAEIOU])', r'\1'), # umlaut
(r'\\~([nNaAoO])', r'\1'), # tilde
(r'\\c\{([cC])\}', r'\1'), # cedilla
(r"\\'{([aeiouAEIOU])}", r'\1'),
(r'\\`{([aeiouAEIOU])}', r'\1'),
(r'\\^{([aeiouAEIOU])}', r'\1'),
(r'\\"{([aeiouAEIOU])}', r'\1'),
(r'\\~{([nNaAoO])}', r'\1'),
]
@classmethod
def normalize_latex(cls, text: str) -> str:
"""Remove LaTeX formatting commands."""
if not text:
return ""
result = text
# Remove LaTeX commands
for pattern, replacement in cls.LATEX_COMMANDS:
result = re.sub(pattern, replacement, result)
# Handle LaTeX accents
for pattern, replacement in cls.LATEX_ACCENTS:
result = re.sub(pattern, replacement, result)
# Replace LaTeX special characters
for latex_char, normal_char in cls.LATEX_CHARS.items():
result = result.replace(latex_char, normal_char)
# Remove remaining braces
result = re.sub(r'[{}]', '', result)
return result
@classmethod
def normalize_unicode(cls, text: str) -> str:
"""Normalize Unicode characters to ASCII."""
if not text:
return ""
# Normalize unicode
text = unicodedata.normalize('NFKD', text)
# Convert to ASCII
text = unidecode(text)
return text
@classmethod
def normalize_whitespace(cls, text: str) -> str:
"""Normalize whitespace."""
if not text:
return ""
# Replace multiple whitespace with single space
text = re.sub(r'\s+', ' ', text)
# Strip leading/trailing whitespace
text = text.strip()
return text
@classmethod
def remove_punctuation(cls, text: str) -> str:
"""Remove punctuation for comparison."""
if not text:
return ""
# Keep alphanumeric and spaces only
return re.sub(r'[^\w\s]', '', text)
@classmethod
def normalize_for_comparison(cls, text: str) -> str:
"""
Full normalization pipeline for text comparison.
Steps:
1. Remove LaTeX formatting
2. Normalize Unicode to ASCII
3. Convert to lowercase
4. Normalize whitespace
5. Remove punctuation
"""
if not text:
return ""
text = cls.normalize_latex(text)
text = cls.normalize_unicode(text)
text = text.lower()
text = cls.normalize_whitespace(text)
text = cls.remove_punctuation(text)
return text
@classmethod
def normalize_author_name(cls, name: str) -> str:
"""
Normalize author name format.
Handles: "Last, First" and "First Last" formats.
Returns: normalized "first last" format.
"""
if not name:
return ""
name = cls.normalize_latex(name)
name = cls.normalize_unicode(name)
name = cls.normalize_whitespace(name)
# Handle "Last, First" format
if ',' in name:
parts = name.split(',', 1)
if len(parts) == 2:
name = f"{parts[1].strip()} {parts[0].strip()}"
name = name.lower()
name = cls.remove_punctuation(name)
return name
@classmethod
def normalize_author_list(cls, authors: str) -> list[str]:
"""
Parse and normalize a list of authors.
Handles "and" as separator and "Last, First" format.
"""
if not authors:
return []
# Split by " and "
author_list = re.split(r'\s+and\s+', authors, flags=re.IGNORECASE)
# Normalize each author
normalized = []
for author in author_list:
normalized_name = cls.normalize_author_name(author.strip())
if normalized_name:
normalized.append(normalized_name)
return normalized
@classmethod
def similarity_ratio(cls, text1: str, text2: str) -> float:
"""
Calculate similarity ratio between two strings.
Uses simple word-based Jaccard similarity.
"""
if not text1 or not text2:
return 0.0
words1 = set(text1.split())
words2 = set(text2.split())
if not words1 and not words2:
return 1.0
if not words1 or not words2:
return 0.0
intersection = words1 & words2
union = words1 | words2
return len(intersection) / len(union)
@classmethod
def levenshtein_similarity(cls, s1: str, s2: str) -> float:
"""Calculate normalized Levenshtein similarity."""
if not s1 and not s2:
return 1.0
if not s1 or not s2:
return 0.0
# Simple Levenshtein implementation
m, n = len(s1), len(s2)
dp = [[0] * (n + 1) for _ in range(m + 1)]
for i in range(m + 1):
dp[i][0] = i
for j in range(n + 1):
dp[0][j] = j
for i in range(1, m + 1):
for j in range(1, n + 1):
if s1[i-1] == s2[j-1]:
dp[i][j] = dp[i-1][j-1]
else:
dp[i][j] = min(dp[i-1][j], dp[i][j-1], dp[i-1][j-1]) + 1
max_len = max(m, n)
distance = dp[m][n]
return 1.0 - (distance / max_len)
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