spaCy/website/docs/api/pipeline-functions.md

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Pipeline Functions	Other built-in pipeline components and helpers	spacy/pipeline.pyx	merge_noun_chunks merge_noun_chunks merge_entities merge_entities merge_subtokens merge_subtokens

merge_noun_chunks

Merge noun chunks into a single token. Also available via the string name "merge_noun_chunks". After initialization, the component is typically added to the processing pipeline using nlp.add_pipe.

Example

texts = [t.token for t in nlp(u"I have a blue car")]
assert texts = ["I", "have", "a", "blue", "car"]

merge_nps = nlp.create_pipe("merge_noun_chunks")
nlp.add_pipe(merge_nps)

texts = [t.token for t in nlp(u"I have a blue car")]
assert texts == ["I", "have", "a blue car"]

Since noun chunks require part-of-speech tags and the dependency parse, make sure to add this component after the "tagger" and "parser" components. By default, nlp.add_pipe will add components to the end of the pipeline and after all other components.

Name	Type	Description
doc	Doc	The Doc object to process, e.g. the Doc in the pipeline.
RETURNS	Doc	The modified Doc with merged noun chunks.

merge_entities

Merge named entities into a single token. Also available via the string name "merge_entities". After initialization, the component is typically added to the processing pipeline using nlp.add_pipe.

Example

texts = [t.token for t in nlp(u"I like David Bowie")]
assert texts = ["I", "like", "David", "Bowie"]

merge_ents = nlp.create_pipe("merge_entities")
nlp.add_pipe(merge_ents)

texts = [t.token for t in nlp(u"I like David Bowie")]
assert texts == ["I", "like", "David Bowie"]

Since named entities are set by the entity recognizer, make sure to add this component after the "ner" component. By default, nlp.add_pipe will add components to the end of the pipeline and after all other components.

Name	Type	Description
doc	Doc	The Doc object to process, e.g. the Doc in the pipeline.
RETURNS	Doc	The modified Doc with merged entities.

merge_subtokens

Merge subtokens into a single token. Also available via the string name "merge_entities". After initialization, the component is typically added to the processing pipeline using nlp.add_pipe.

As of v2.1, the parser is able to predict "subtokens" that should be merged into one single token later on. This is especially relevant for languages like Chinese, Japanese or Korean, where a "word" isn't defined as a whitespace-delimited sequence of characters. Under the hood, this component uses the Matcher to find sequences of tokens with the dependency label "subtok" and then merges them into a single token.

Example

Note that this example assumes a custom Chinese model that oversegments and was trained to predict subtokens.

doc = nlp("拜托")
print([(token.text, token.dep_) for token in doc])
# [('拜', 'subtok'), ('托', 'subtok')]

merge_subtok = nlp.create_pipe("merge_subtokens")
nlp.add_pipe(merge_subtok)

doc = nlp("拜托")
print([token.text for token in doc])
# ['拜托']

Since subtokens are set by the parser, make sure to add this component after the "parser" component. By default, nlp.add_pipe will add components to the end of the pipeline and after all other components.

Name	Type	Description
doc	Doc	The Doc object to process, e.g. the Doc in the pipeline.
label	unicode	The subtoken dependency label. Defaults to "subtok".
RETURNS	Doc	The modified Doc with merged subtokens.