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Corrected Text has 12 facts recorded in Dontopedia across 6 references, with 1 live disagreement.
Mostly:rdf:type(4), description(1), used as both input and output(1)
usedAsBothInputAndOutputcreatedFromdecodedByisVariableappendToassignedByOther subjects in dontopedia point AT this entity as a value. These are inverse relationships — e.g. “X motherOf this subject” — and answer questions the forward facts can't. Grouped by predicate.
returnsReturns(4)ex:context__aware_correctionex:context_aware_correctionex:spell_correctionex:spelling_correctionoutputsOutputs(2)ex:print_functionex:print-statementappendAppend(1)ex:corrected_wordsappendsAppends(1)ex:corrected_wordsargumentArgument(1)ex:print_statementassignsToAssigns to(1)ex:variable-assignmentcontainsContains(1)ex:example_usageconvertsTokensToIdsConverts Tokens to Ids(1)ex:text-decodingdecodesDecodes(1)ex:text-decodingisAppendedToIs Appended to(1)ex:corrected_wordsreceivesArgumentReceives Argument(1)ex:print-functionTimeline axis is valid_time — when each source says the fact was true in the world, not when Dontopedia learned about it. Retracted rows are kept for provenance; coloured stripes indicate the context kind.
doc:beam/4346daa8-69e0-41ac-a434-f64d60c67428corrected_text = context_aware_correction(input_text) corrected_words.append(corrected_text) return ' '.join(corrected_words) ``` #### 5. Parallel Processing ```python from concurrent.futures import Th…
doc:beam/3a72d946-b8c4-4912-8fdb-b78740854153corrected_text = tokenizer.decode(corrected_text) return corrected_text def spell_correction(input_text): """ Combine dictionary lookups and context-aware correction. """ words_list = word_tokenize(input_text) c…
doc:beam/3cb97947-2304-4ba1-a2c5-598750f9b2f9dist = distance(word, dict_word) if dist < min_distance and dist <= threshold: min_distance = dist closest_word = dict_word return closest_word tokenizer = BertTokenizer.from_pretrained('bert-bas…
doc:beam/81b08382-6139-462b-a047-4231b5c0a4bbdp[i][j] = dp[i - 1][j - 1] else: dp[i][j] = 1 + min(dp[i - 1][j], dp[i][j - 1], dp[i - 1][j - 1]) return dp[len1][len2] def spelling_correction(input_text): """Apply spelling correction…
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