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Multi-Word Token (MWT) Expansion

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Description

The Multi-Word Token (MWT) expansion module can expand a raw token into multiple syntactic words, which makes it easier to carry out Universal Dependencies analysis in some languages. This was handled by the MWTProcessor in Stanza, and can be invoked with the name mwt. The token upon which an expansion will be performed is predicted by the TokenizeProcessor, before the invocation of the MWTProcessor.

For more details on why MWT is necessary for Universal Dependencies analysis, please visit the UD tokenization page.

NameAnnotator class nameRequirementGenerated AnnotationDescription
mwtMWTProcessortokenizeExpands multi-word tokens (MWTs) into multiple words when they are predicted by the tokenizer. Each Token will correspond to one or more Words after tokenization and MWT expansion.Expands multi-word tokens (MWT) predicted by the TokenizeProcessor. This is only applicable to some languages.

Options

Option nameTypeDefaultDescription
mwt_batch_sizeint50When annotating, this argument specifies the maximum number of words to process as a minibatch for efficient processing.
Caveat: the larger this number is, the more working memory is required (main RAM or GPU RAM, depending on the computating device).

Example Usage

The MWTProcessor processor only requires TokenizeProcessor to be run before it. After these two processors have processed the text, the Sentences will have lists of Tokens and corresponding syntactic Words based on the multi-word-token expander model. The list of tokens for a sentence sent can be accessed with sent.tokens, and its list of words with sent.words. Similarly, the list of words for a token token can be accessed with token.words.

Accessing Syntactic Words for Multi-Word Token

Here is an example of a piece of text in French that requires multi-word token expansion, and how to access the underlying words of these multi-word tokens:

import stanza

nlp = stanza.Pipeline(lang='fr', processors='tokenize,mwt')
doc = nlp('Nous avons atteint la fin du sentier.')
for token in doc.sentences[0].tokens:
    print(f'token: {token.text}\twords: {", ".join([word.text for word in token.words])}')

As a result of running this code, we see that the word du is expanded into its underlying syntactic words, de and le.

token: Nous     words: Nous
token: avons    words: avons
token: atteint  words: atteint
token: la       words: la
token: fin      words: fin
token: du       words: de, le
token: sentier  words: sentier
token: .        words: .

Accessing Parent Token for Word

When performing word-level annotations and processing, it might sometimes be useful to access the token a given word is derived from, so that we can access its character offsets, among other things, that are associated with the token. Here is an example of how to do that with Word’s parent property with the same sentence we just saw:

import stanza

nlp = stanza.Pipeline(lang='fr', processors='tokenize,mwt')
doc = nlp('Nous avons atteint la fin du sentier.')
for word in doc.sentences[0].words:
    print(f'word: {word.text}\tparent token: {word.parent.text}')

As one can see in the result below, Words de and le have the same parent token du.

word: Nous      parent token: Nous
word: avons     parent token: avons
word: atteint   parent token: atteint
word: la        parent token: la
word: fin       parent token: fin
word: de        parent token: du
word: le        parent token: du
word: sentier   parent token: sentier
word: .         parent token: .

Training-Only Options

Most training-only options are documented in the argument parser of the MWT expander.