seq = difflib.SequenceMatcher(a=self.lookup_word, b=norm)
distance = 0
for tag, afrom, ato, bfrom, bto in seq.get_opcodes():
- if tag == 'delete' and (afrom == 0 or ato == len(self.lookup_word)):
+ if tag in ('delete', 'insert') and (afrom == 0 or ato == len(self.lookup_word)):
distance += 1
elif tag == 'replace':
distance += max((ato-afrom), (bto-bfrom))
async def setup(self) -> None:
""" Set up static data structures needed for the analysis.
"""
- rules = await self.conn.get_property('tokenizer_import_normalisation')
- self.normalizer = Transliterator.createFromRules("normalization", rules)
- rules = await self.conn.get_property('tokenizer_import_transliteration')
- self.transliterator = Transliterator.createFromRules("transliteration", rules)
+ async def _make_normalizer() -> Any:
+ rules = await self.conn.get_property('tokenizer_import_normalisation')
+ return Transliterator.createFromRules("normalization", rules)
+
+ self.normalizer = await self.conn.get_cached_value('ICUTOK', 'normalizer',
+ _make_normalizer)
+
+ async def _make_transliterator() -> Any:
+ rules = await self.conn.get_property('tokenizer_import_transliteration')
+ return Transliterator.createFromRules("transliteration", rules)
+
+ self.transliterator = await self.conn.get_cached_value('ICUTOK', 'transliterator',
+ _make_transliterator)
if 'word' not in self.conn.t.meta.tables:
sa.Table('word', self.conn.t.meta,
standardized form search will work with. All information removed
at this stage is inevitably lost.
"""
- return cast(str, self.normalizer.transliterate(text))
+ norm = cast(str, self.normalizer.transliterate(text))
+ numspaces = norm.count(' ')
+ if numspaces > 4 and len(norm) <= (numspaces + 1) * 3:
+ return ''
+
+ return norm
def split_query(self, query: qmod.QueryStruct) -> Tuple[QueryParts, WordDict]: