Lemmatization

Description
Options
Example Usage
Training-Only Options

Description

The lemmatization module recovers the lemma form for each input word. For example, the input sequence “I ate an apple” will be lemmatized into “I eat a apple”. This type of word normalization is useful in many real-world applications. In Stanza, lemmatization is performed by the LemmaProcessor and can be invoked with the name lemma.

Name	Annotator class name	Requirement	Generated Annotation	Description
lemma	LemmaProcessor	tokenize, mwt, pos	Perform lemmatization on a `Word` using the `Word.text` and `Word.upos` values. The result can be accessed as `Word.lemma`.	Generates the word lemmas for all words in the Document.

Options

Option name	Type	Default	Description
lemma_use_identity	bool	`False`	When this flag is used, an identity lemmatizer (see `models.identity_lemmatizer`) will be used instead of a statistical lemmatizer. This is useful when [`Word.lemma`] is required for languages such as Vietnamese, where the lemma is identical to the original word form.
lemma_batch_size	int	50	When annotating, this argument specifies the maximum number of words to batch for efficient processing.
lemma_ensemble_dict	bool	`True`	If set to `True`, the lemmatizer will ensemble a seq2seq model with the output from a dictionary-based lemmatizer, which yields improvements on many languages (see system description paper for more details).
lemma_dict_only	bool	`False`	If set to `True`, only a dictionary-based lemmatizer will be used. For languages such as Chinese, a dictionary-based lemmatizer is enough.
lemma_edit	bool	`True`	If set to `True`, use an edit classifier alongside the seq2seq lemmatizer. The edit classifier will predict “shortcut” operations such as “identical” or “lowercase”, to make the lemmatization of long sequences more stable.
lemma_beam_size	int	1	Control the beam size used during decoding in the seq2seq lemmatizer.
lemma_pretagged	bool	`False`	Assume the document is tokenized and pretagged. Only run lemma analysis on the document.
lemma_max_dec_len	int	50	Control the maximum decoding character length in the seq2seq lemmatizer. The decoder will stop if this length is achieved and the end-of-sequence character is still not seen.

Example Usage

Running the LemmaProcessor requires the TokenizeProcessor, MWTProcessor, and POSProcessor. After the pipeline is run, the Document will contain a list of Sentences, and the Sentences will contain lists of Words. The lemma information can be found in the lemma field of each Word.

Accessing Lemma for Word

Here is an example of lemmatizing words in a sentence and accessing their lemmas afterwards:

import stanza

nlp = stanza.Pipeline(lang='en', processors='tokenize,mwt,pos,lemma')
doc = nlp('Barack Obama was born in Hawaii.')
print(*[f'word: {word.text+" "}\tlemma: {word.lemma}' for sent in doc.sentences for word in sent.words], sep='\n')

As can be seen in the result, Stanza lemmatizes the word was as be.

word: Barack    lemma: Barack
word: Obama     lemma: Obama
word: was       lemma: be
word: born      lemma: bear
word: in        lemma: in
word: Hawaii    lemma: Hawaii
word: .         lemma: .

Lemmatizing pretagged text

If you already have tokenized, tagged text, you can use the lemmatizer to add lemmas without retokenizing or tagging:

import stanza
from stanza.models.common.doc import Document

nlp = stanza.Pipeline(lang='en', processors='tokenize,lemma', lemma_pretagged=True, tokenize_pretokenized=True)
pp = Document([[{'id': 1, 'text': 'puppies', 'upos': 'NOUN'}]])
print("BEFORE ADDING LEMMA")
print(pp)
doc = nlp(pp)
print("AFTER ADDING LEMMA")
print(doc)

The updated doc will have the lemmas attached to the words:

BEFORE ADDING LEMMA
[
  [
    {
      "id": 1,
      "text": "puppies",
      "upos": "NOUN"
    }
  ]
]
AFTER ADDING LEMMA
[
  [
    {
      "id": 1,
      "text": "puppies",
      "lemma": "puppy",
      "upos": "NOUN"
    }
  ]
]

Improving the Lemmatizer by Providing Key-Value Dictionary

It is possible to improve the lemmatizer by providing a key-value dictionary. Lemmatizer will check it first and then use statistical model if the word is not in dictionary.

First, load your downloaded lemmatizer model. For English lemmatizer using ewt package, it can be found at ~/stanza_resources/en/lemma/ewt.pt.

Second, customize two dictionaries: 1) composite_dict which maps (word, pos) to lemma; 2) word_dict which maps word to lemma. The lemmatizer will first check the composite dictionary, then word dictionary.

Finally, save your customized model and load it with Stanza.

Here is an example of customizing the lemmatizer by providing a key-value dictionary:

# Load word_dict and composite_dict
import torch
model = torch.load('~/stanza_resources/en/lemma/ewt.pt', map_location='cpu')
word_dict, composite_dict = model['dicts']

# Customize your own dictionary
composite_dict[('myword', 'NOUN')] = 'mylemma'
word_dict['myword'] = 'mylemma'

# Save your model
torch.save(model, '~/stanza_resources/en/lemma/ewt_customized.pt')

# Load your customized model with Stanza
import stanza
nlp = stanza.Pipeline('en', package='ewt', processors='tokenize,pos,lemma', lemma_model_path='~/stanza_resources/en/lemma/ewt_customized.pt'
print(nlp('myword')) # Should get lemma 'mylemma'

As can be seen in the result, Stanza should lemmatize the word myword as mylemma.

Training-Only Options

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