Dontopedia

stopwords

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-07.)

stopwords has 10 facts recorded in Dontopedia across 3 references, with 1 live disagreement.

10 facts·7 predicates·3 sources·1 in dispute

Mostly:rdf:type(3), part of(1), module location(1)

Maturity scale raw canonical shape-checked rule-derived certified

Inbound mentions (4)

Other 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.

assignedByAssigned by(1)

excludesExcludes(1)

excludesTypeExcludes Type(1)

importsImports(1)

Other facts (9)

The 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.

9 facts
PredicateValueRef
Rdf:typeCorpus[1]
Rdf:typeCorpus Resource[2]
Rdf:typeWord Category[3]
Part ofNltk.corpus[1]
Module Locationnltk.corpus[2]
Is Imported Fromnltk.corpus[2]
Languageenglish[2]
Inverse ofNltk.corpus[2]
Language SpecificEnglish Language[2]

Timeline

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.

typebeam/407031c6-8e67-411e-a5b3-fe9a2898c457
ex:Corpus
partOfbeam/407031c6-8e67-411e-a5b3-fe9a2898c457
ex:nltk.corpus
typebeam/9da27bd6-4d72-425e-a89c-dc2a4d657e13
ex:CorpusResource
labelbeam/9da27bd6-4d72-425e-a89c-dc2a4d657e13
stopwords
moduleLocationbeam/9da27bd6-4d72-425e-a89c-dc2a4d657e13
nltk.corpus
isImportedFrombeam/9da27bd6-4d72-425e-a89c-dc2a4d657e13
nltk.corpus
languagebeam/9da27bd6-4d72-425e-a89c-dc2a4d657e13
english
inverseOfbeam/9da27bd6-4d72-425e-a89c-dc2a4d657e13
ex:nltk.corpus
languageSpecificbeam/9da27bd6-4d72-425e-a89c-dc2a4d657e13
ex:english-language
typebeam/45c60563-8279-420f-bfa8-33f0a2e6896e
ex:WordCategory

References (3)

3 references
  1. ctx:claims/beam/407031c6-8e67-411e-a5b3-fe9a2898c457
    • full textbeam-chunk
      text/plain1 KBdoc:beam/407031c6-8e67-411e-a5b3-fe9a2898c457
      Show excerpt
      text_en = "Apple is looking at buying U.K. startup for $1 billion." text_es = "La empresa Apple comprara una startup britanica por mil millones de dolares." print(process_text(text_en)) print(process_text(text_es)) ``` ### 3. **
  2. ctx:claims/beam/9da27bd6-4d72-425e-a89c-dc2a4d657e13
    • full textbeam-chunk
      text/plain1 KBdoc:beam/9da27bd6-4d72-425e-a89c-dc2a4d657e13
      Show excerpt
      NLTK is a leading platform for building Python programs to work with human language data. It provides easy-to-use interfaces to over 50 corpora and lexical resources such as WordNet, along with a suite of text processing libraries for class
  3. ctx:claims/beam/45c60563-8279-420f-bfa8-33f0a2e6896e
    • full textbeam-chunk
      text/plain1 KBdoc:beam/45c60563-8279-420f-bfa8-33f0a2e6896e
      Show excerpt
      2. **Tokenization**: The `doc` object contains the processed text, and you can extract tokens, filtered tokens (without stopwords), and lemmatized tokens. 3. **Performance Measurement**: The example measures the time taken to preprocess a l

See also

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