About cookies on this site Our websites require some cookies to function properly (required). In addition, other cookies may be used with your consent to analyze site usage, improve the user experience and for advertising. For more information, please review your options. By visiting our website, you agree to our processing of information as described in IBM’sprivacy statement. To provide a smooth navigation, your cookie preferences will be shared across the IBM web domains listed here.
Last updated: Jul 25, 2024
The Watson Natural Language Processing Noun phrase extraction block extracts noun phrases from input text.
Block name
noun-phrases_rbr_<language>_stock
Note: The "rbr" abbreviation in model name means rule-based reasoning. RBR models handle syntactically regular entity types such as number, email and phone.
Supported languages
Noun phrase extraction is available for the following languages. For a list of the language codes and the corresponding language, see Language codes.
ar, cs, da, de, es, en, fi, fr, he, hi, it, ja, ko, nb, nl, nn, pt, ro, ru, sk, sv, tr, zh_cn, zh_tw
Capabilities
The Noun phrase extraction block extracts non-overlapping noun phrases from the input text.
Capabilities | Examples |
---|---|
Extraction of non-overlapping noun phrases | "Anna went to school at University of California Santa Cruz" -> Anna, school, University of California Santa Cruz |
Dependencies on other blocks
None
Code sample
import watson_nlp
# Load the model for English
noun_phrases_model = watson_nlp.load('noun-phrases_rbr_en_stock')
# Run the model on the input text
noun_phrases = noun_phrases_model.run('Anna went to school at University of California Santa Cruz')
print(noun_phrases)
Output of the code sample:
{ "noun_phrases": [ { "span": { "begin": 0, "end": 4, "text": "Anna" } }, { "span": { "begin": 13, "end": 19, "text": "school" } }, { "span": { "begin": 23, "end": 58, "text": "University of California Santa Cruz" } } ], "producer_id": { "name": "RBR Noun phrases", "version": "0.0.1" } }
Parent topic: Watson Natural Language Processing task catalog
Was the topic helpful?
0/1000