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import re
from pathlib import Path
from typing import List, Dict, Optional, Union
class OldEnglishDictionary:
"""A class to query the aligned Old English Dictionary dataset."""
def __init__(self, data_path: str = "data/unified_index.tab"):
"""Initialize the dictionary with the unified index.
Args:
data_path: Path to the unified_index.tab file
"""
self.data_path = Path(data_path)
self.df = pd.read_csv(self.data_path, sep='\t')
# Fill NaN values with empty strings for easier processing
self.df = self.df.fillna('')
# Create lowercase versions of key columns for case-insensitive search
self.df['canonical_key_lower'] = self.df['canonical_key'].str.lower()
self.df['normalised_form_lower'] = self.df['normalised_form'].str.lower()
# Dictionary sources
self.sources = ['BT', 'CH', 'Sweet', 'DOE', 'ParCorOE']
# Count total entries
self.total_entries = len(self.df)
print(f"Loaded {self.total_entries} aligned entries from {self.data_path}")
def search(self, query: str,
mode: str = 'canonical',
exact: bool = False,
pos: Optional[str] = None,
source: Optional[str] = None) -> pd.DataFrame:
"""Search for entries matching the query.
Args:
query: The search term
mode: Search mode ('canonical', 'normalised', 'headword')
exact: If True, match the entire term; if False, use substring matching
pos: Filter by part of speech
source: Filter by dictionary source
Returns:
DataFrame with matching entries
"""
query = query.lower()
# Base query logic
if mode == 'canonical':
if exact:
results = self.df[self.df['canonical_key_lower'] == query]
else:
results = self.df[self.df['canonical_key_lower'].str.contains(query, regex=False)]
elif mode == 'normalised':
if exact:
results = self.df[self.df['normalised_form_lower'] == query]
else:
results = self.df[self.df['normalised_form_lower'].str.contains(query, regex=False)]
elif mode == 'headword':
# Search across all dictionary headword columns
mask = pd.Series(False, index=self.df.index)
for source in self.sources:
col = f"{source}_headword"
if col in self.df.columns:
if exact:
mask |= (self.df[col].str.lower() == query)
else:
mask |= (self.df[col].str.lower().str.contains(query, regex=False, na=False))
results = self.df[mask]
else:
raise ValueError(f"Invalid search mode: {mode}. Use 'canonical', 'normalised', or 'headword'.")
# Apply filters
if pos:
results = results[results['POS'].str.lower() == pos.lower()]
if source:
col = f"{source}_headword"
if col in self.df.columns:
results = results[results[col] != '']
else:
raise ValueError(f"Invalid source: {source}. Use one of {self.sources}")
return results
def display_entry(self, entry: pd.Series) -> None:
"""Display a single dictionary entry in a readable format.
Args:
entry: A single row from the dataframe
"""
print("\n" + "=" * 80)
print(f"CANONICAL KEY: {entry['canonical_key']}")
print(f"NORMALIZED FORM: {entry['normalised_form']}")
print(f"MATCH TYPE: {entry['match_type']}")
if entry['POS']:
print(f"PART OF SPEECH: {entry['POS']}")
if entry['Gloss']:
print(f"GLOSS: {entry['Gloss']}")
print("-" * 80)
print("DICTIONARY HEADWORDS:")
for source in self.sources:
col = f"{source}_headword"
if col in entry and entry[col]:
print(f" {source}: {entry[col]}")
print("=" * 80)
def display_results(self, results: pd.DataFrame, limit: int = 10) -> None:
"""Display search results in a readable format.
Args:
results: DataFrame containing search results
limit: Maximum number of results to display
"""
num_results = len(results)
if num_results == 0:
print("No matching entries found.")
return
print(f"\nFound {num_results} matching entries.")
if num_results > limit:
print(f"Displaying first {limit} results.")
results = results.head(limit)
for _, entry in results.iterrows():
self.display_entry(entry)
def find_similar(self, query: str, method: str = 'sound') -> pd.DataFrame:
"""Find entries similar to the query using various similarity methods.
Args:
query: The search term
method: The similarity method ('sound', 'edit', 'prefix')
Returns:
DataFrame with similar entries
"""
query = query.lower()
if method == 'sound':
# Apply Ellis vocalic transformations similar to those used in alignment
variations = self._generate_ellis_variations(query)
mask = pd.Series(False, index=self.df.index)
for variation in variations:
mask |= (self.df['canonical_key_lower'] == variation)
return self.df[mask]
elif method == 'edit':
# Crude edit distance approximation - finding entries
# that share at least half their characters with the query
query_set = set(query)
results = []
for idx, row in self.df.iterrows():
canonical = row['canonical_key_lower']
# Skip very short terms or terms with big length difference
if len(canonical) < 3 or abs(len(canonical) - len(query)) > 3:
continue
canonical_set = set(canonical)
intersection = len(query_set.intersection(canonical_set))
union = len(query_set.union(canonical_set))
# Jaccard similarity threshold
if intersection / union > 0.6:
results.append(idx)
return self.df.loc[results]
elif method == 'prefix':
# Find entries that start with the query
return self.df[self.df['canonical_key_lower'].str.startswith(query)]
else:
raise ValueError(f"Invalid method: {method}. Use 'sound', 'edit', or 'prefix'.")
def _generate_ellis_variations(self, word: str) -> List[str]:
"""Generate variations based on Ellis vocalic correspondences.
Args:
word: The input word
Returns:
List of phonologically plausible variations
"""
variations = [word]
# Ellis vocalic correspondences
transformations = [
(r'ie', r'y'), # fierd/fyrd
(r'y', r'ie'), # fyrd/fierd
(r'ie', r'i'), # diere/dire
(r'i', r'ie'), # diren/dieren
(r'io', r'eo'), # bion/beon
(r'eo', r'io'), # beorht/biorht
(r'on', r'an'), # monig/manig
(r'an', r'on'), # manig/monig
(r'om', r'am'), # from/fram
(r'am', r'om'), # fram/from
(r'rg', r'rh'), # burg/burh
(r'rh', r'rg'), # burh/burg
(r'ea', r'a'), # eald/ald
(r'a', r'ea'), # ald/eald
(r'eo', r'e') # eofot/efot
]
for pattern, replacement in transformations:
if pattern in word:
variations.append(word.replace(pattern, replacement))
return variations
def get_statistics(self) -> Dict[str, Union[int, float]]:
"""Get statistics about the dictionary dataset.
Returns:
Dictionary with statistics
"""
stats = {
"total_entries": self.total_entries,
"multi_source_entries": len(self.df[self.df['match_type'] != 'single']),
"exact_matches": len(self.df[self.df['match_type'] == 'exact']),
"ellis_matches": len(self.df[self.df['match_type'] == 'ellis_vocalic']),
"single_source": len(self.df[self.df['match_type'] == 'single'])
}
# Add source-specific counts
for source in self.sources:
col = f"{source}_headword"
if col in self.df.columns:
stats[f"{source}_entries"] = len(self.df[self.df[col] != ''])
# Add POS distribution
pos_counts = self.df['POS'].value_counts().to_dict()
stats["pos_distribution"] = pos_counts
return stats
# Example usage
if __name__ == "__main__":
oe_dict = OldEnglishDictionary()
# Simple demo of the search functionality
print("\n=== SEARCH DEMONSTRATION ===")
# Search by canonical key
print("\nSearching for entries with canonical key containing 'abelgan'...")
results = oe_dict.search("abelgan", mode="canonical")
oe_dict.display_results(results)
# Search by normalized form
print("\nSearching for entries with normalized form containing 'ābēodan'...")
results = oe_dict.search("ābēodan", mode="normalised", exact=True)
oe_dict.display_results(results)
# Search by headword across all dictionaries
print("\nSearching for entries where any dictionary has headword containing 'cyning'...")
results = oe_dict.search("cyning", mode="headword")
oe_dict.display_results(results, limit=5)
# Filter by part of speech
print("\nSearching for noun entries with canonical key containing 'helm'...")
results = oe_dict.search("helm", mode="canonical", pos="N")
oe_dict.display_results(results, limit=5)
# Find similar words
print("\nFinding entries similar to 'beon' using sound correspondences...")
results = oe_dict.find_similar("beon", method="sound")
oe_dict.display_results(results)
# Show dictionary statistics
stats = oe_dict.get_statistics()
print("\n=== DICTIONARY STATISTICS ===")
for key, value in stats.items():
if key != "pos_distribution":
print(f"{key}: {value}")
print("\nPart of Speech Distribution:")
for pos, count in stats["pos_distribution"].items():
if pos: # Skip empty POS
print(f" {pos}: {count}") |