Token Attributes
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Token Attributes has 5 facts recorded in Dontopedia across 2 references, with 1 live disagreement.
Maturity scale
raw canonical shape-checked rule-derived certifiedOther facts (5)
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.
| Predicate | Value | Ref |
|---|---|---|
| Includes Attribute | Text Attribute | [1] |
| Includes Attribute | Lemma Attribute | [1] |
| Includes Attribute | Pos Attribute | [1] |
| Rdf:type | Linguistic Attributes | [1] |
| Accessed Via | dot-notation | [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.
References (2)
ctx:claims/beam/18306c1f-b51a-45dd-b169-e340e3696b52- full textbeam-chunktext/plain1 KB
doc:beam/18306c1f-b51a-45dd-b169-e340e3696b52Show excerpt
Now, let's tokenize some text and visualize the process for debugging. ```python # Sample text text = "Hello, world! This is a test sentence with [custom] tokens." # Process the text doc = nlp(text) # Print the tokens for token in doc: …
ctx:claims/beam/19c50864-0395-4826-b4c8-6b6c2fab4d44- full textbeam-chunktext/plain1 KB
doc:beam/19c50864-0395-4826-b4c8-6b6c2fab4d44Show excerpt
return 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] …
See also
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