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

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

merge_noun_chunks

Merge noun chunks into a single token. Also available via the string name "merge_noun_chunks".

Example

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

nlp.add_pipe("merge_noun_chunks")
texts = [t.text for t in nlp("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	Description
doc	The Doc object to process, e.g. the Doc in the pipeline. Doc
RETURNS	The modified Doc with merged noun chunks. Doc

merge_entities

Merge named entities into a single token. Also available via the string name "merge_entities".

Example

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

nlp.add_pipe("merge_entities")

texts = [t.text for t in nlp("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	Description
doc	The Doc object to process, e.g. the Doc in the pipeline. Doc
RETURNS	The modified Doc with merged entities. Doc

merge_subtokens

Merge subtokens into a single token. Also available via the string name "merge_subtokens". 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')]

nlp.add_pipe("merge_subtokens")
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	Description
doc	The Doc object to process, e.g. the Doc in the pipeline. Doc
label	The subtoken dependency label. Defaults to "subtok". str
RETURNS	The modified Doc with merged subtokens. Doc

token_splitter

Split tokens longer than a minimum length into shorter tokens. Intended for use with transformer pipelines where long spaCy tokens lead to input text that exceed the transformer model max length. See managing transformer model max length limitations.

Example

config = {"min_length": 20, "split_length": 5}
nlp.add_pipe("token_splitter", config=config, first=True)
doc = nlp("aaaaabbbbbcccccdddddee")
print([token.text for token in doc])
# ['aaaaa', 'bbbbb', 'ccccc', 'ddddd', 'ee']

Setting	Description
min_length	The minimum length for a token to be split. Defaults to 25. int
split_length	The length of the split tokens. Defaults to 5. int
RETURNS	The modified Doc with the split tokens. Doc