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Detect Language has 82 facts recorded in Dontopedia across 9 references, with 9 live disagreements.
Mostly:rdf:type(9), rdfs:label(7), has parameter(6)
hasParameterreturnshandlesExceptionreturnsOnExceptionusesLibrarycalledBycalledBeforecatchesrdfs:labelaccessesPropertycalledWithinOther 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.
callsFunctionCalls Function(3)ex:example_usageex:preprocess_queryex:test_full_pipelinecontainsFunctionContains Function(2)ex:codeBlockex:text_processing_pipelineusedByUsed by(2)ex:langdetect-libraryex:logging-librarycalledBeforeCalled Before(1)ex:preprocess_textdependsOnDepends on(1)ex:tokenize_textfollowsFollows(1)ex:tokenize_texthasStepHas Step(1)ex:text_processing_pipelineinputForInput for(1)ex:cleaned_textoutputOfOutput of(1)ex:detected_langprecedesPrecedes(1)ex:preprocess_texttestsFunctionTests Function(1)ex:TestDetectLanguageusedByFunctionUsed by Function(1)ex:langdetectusesFunctionUses Function(1)ex:example_usageverifiesFunctionVerifies Function(1)ex:test_detect_languageThe long tail: predicates that appear too rarely to warrant their own section. Filter or scroll to find a specific one. Each row links to its source.
| Predicate | Value | Ref |
|---|---|---|
| Invocation Count | 900 | [1] |
| Error Behavior | return_empty_string | [1] |
| Binds Exception | e | [1] |
| Catches Exception | Exception | [1] |
| Uses Try Except | true | [1] |
| Inverse Has Parameter | text_obj | [1] |
| Logs Error | Logging | [1] |
| Has Error Handling | true | [1] |
| Extracts Property | language.code | [1] |
| Purpose | Language Identification | [3] |
| Has Logging Call | Detection Debug Log | [2] |
| Assigns | Lang Variable | [2] |
| Part of | Python Code | [2] |
| Has Try Block | Try Block | [2] |
| Calls | Langdetect Detect | [2] |
| Except Block | Detector-instantiation | [7] |
| Try Block | langdetect.detect | [7] |
| Parameter Name | text | [7] |
| Enables | language_specific_tokenization | [7] |
| Exception Type | langdetect.LangDetectException | [7] |
| Follows | Preprocess Text | [7] |
| Secondary Method | Detector | [7] |
| Primary Method | langdetect.detect | [7] |
| Error Handling | try-except-block | [7] |
| Precedes | Tokenize Text | [7] |
| Fallback Uses | Detector | [7] |
| Uses | langdetect.detect | [7] |
| Returns Default Value on Exception | unknown | [5] |
| Produces | detected_lang | [5] |
| Contains Except Block | true | [5] |
| Contains Try Block | true | [5] |
| Decorated With | cache.cache | [6] |
| Exception Return Value | unknown | [6] |
| Returns Language Code | true | [6] |
| Uses Cache | true | [6] |
| Description | Detects language of input text using langdetect | [6] |
| Imports | Langdetect | [4] |
Timeline 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/33a7d6c0-6888-46e3-b0de-c6368c12c02a# Detect the language language = text_obj.language.code return language except Exception as e: logging.error(f"Error detecting language for text: {text}. Error: {str(e)}") return…
doc:beam/f8068905-8522-4e7a-9746-bbad05dbfbde- Regularly review the codebase to identify and refactor complex or error-prone sections. - Simplify logic and improve readability to reduce the likelihood of bugs. ### Example Implementation Let's go through an example implementati…
doc:beam/e50e1439-fa74-447d-ba48-a7a4b6694859cleaned_text = re.sub(r"(\bcan't\b)", "cannot", cleaned_text) return cleaned_text def detect_language(text): try: lang = langdetect.detect(text) return lang except langdetect.LangDetectException: ret…
doc:beam/19c50864-0395-4826-b4c8-6b6c2fab4d44return lang def tokenize_text(text, lang): if lang == 'en': doc = nlp_en(text) tokens = [token.text for token in doc] elif lang == 'es': doc = nlp_es(text) tokens = [token.text for token in doc] …
doc:beam/5eed31af-31d4-479c-87b1-892ac460fe9ddetected_lang = detect_language(cleaned_text) tokens = tokenize_text(cleaned_text, detected_lang) final_tokens = postprocess_tokens(tokens) print(final_tokens) ``` ### Explanation 1. **Cache Initialization**: - `cache_location`: Specif…
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