`models.translation_matrix` – Translation Matrix model¶

Produce translation matrix to translate the word from one language to another language, using either standard nearest neighbour method or globally corrected neighbour retrieval method 1.

This method can be used to augment the existing phrase tables with more candidate translations, or filter out errors from the translation tables and known dictionaries 2. What’s more, It also work for any two sets of named-vectors where there are some paired-guideposts to learn the transformation.

Examples

How to make translation between two set of word-vectors¶

Initialize a word-vector models

>>> from gensim.models import KeyedVectors
>>> from gensim.test.utils import datapath
>>>
>>> model_en = KeyedVectors.load_word2vec_format(datapath("EN.1-10.cbow1_wind5_hs0_neg10_size300_smpl1e-05.txt"))
>>> model_it = KeyedVectors.load_word2vec_format(datapath("IT.1-10.cbow1_wind5_hs0_neg10_size300_smpl1e-05.txt"))

Define word pairs (that will be used for construction of translation matrix

>>> word_pairs = [
...     ("one", "uno"), ("two", "due"), ("three", "tre"), ("four", "quattro"), ("five", "cinque"),
...     ("seven", "sette"), ("eight", "otto"),
...     ("dog", "cane"), ("pig", "maiale"), ("fish", "cavallo"), ("birds", "uccelli"),
...     ("apple", "mela"), ("orange", "arancione"), ("grape", "acino"), ("banana", "banana")
... ]

Fit TranslationMatrix

>>> trans_model = TranslationMatrix(model_en, model_it, word_pairs=word_pairs)

Apply model (translate words “dog” and “one”)

>>> trans_model.translate(["dog", "one"], topn=3)
OrderedDict([('dog', [u'cane', u'gatto', u'cavallo']), ('one', [u'uno', u'due', u'tre'])])

Save / load model

>>> with temporary_file("model_file") as fname:
...     trans_model.save(fname)  # save model to file
...     loaded_trans_model = TranslationMatrix.load(fname)  # load model

How to make translation between two `Doc2Vec` models¶

Prepare data and models

>>> from gensim.test.utils import datapath
>>> from gensim.test.test_translation_matrix import read_sentiment_docs
>>> from gensim.models import Doc2Vec
>>>
>>> data = read_sentiment_docs(datapath("alldata-id-10.txt"))[:5]
>>> src_model = Doc2Vec.load(datapath("small_tag_doc_5_iter50"))
>>> dst_model = Doc2Vec.load(datapath("large_tag_doc_10_iter50"))

Train backward translation

>>> model_trans = BackMappingTranslationMatrix(data, src_model, dst_model)
>>> trans_matrix = model_trans.train(data)

Apply model

>>> result = model_trans.infer_vector(dst_model.dv[data[3].tags])

References

1(1,2): Dinu, Georgiana, Angeliki Lazaridou, and Marco Baroni. “Improving zero-shot learning by mitigating the hubness problem”, https://arxiv.org/abs/1412.6568
2: Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean. “Distributed Representations of Words and Phrases and their Compositionality”, https://arxiv.org/abs/1310.4546

class gensim.models.translation_matrix.BackMappingTranslationMatrix(source_lang_vec, target_lang_vec, tagged_docs=None, random_state=None)¶

Bases: gensim.utils.SaveLoad

Realize the BackMapping translation matrix which map the source model’s document vector to the target model’s document vector(old model).

BackMapping translation matrix is used to learn a mapping for two document vector space which we specify as source document vector and target document vector. The target document vector are trained on superset corpus of source document vector, we can incrementally increase the vector in the old model through the BackMapping translation matrix.

the details use seen the notebook 3.

Examples

>>> from gensim.test.utils import datapath
>>> from gensim.test.test_translation_matrix import read_sentiment_docs
>>> from gensim.models import Doc2Vec, BackMappingTranslationMatrix
>>>
>>> data = read_sentiment_docs(datapath("alldata-id-10.txt"))[:5]
>>> src_model = Doc2Vec.load(datapath("small_tag_doc_5_iter50"))
>>> dst_model = Doc2Vec.load(datapath("large_tag_doc_10_iter50"))
>>>
>>> model_trans = BackMappingTranslationMatrix(src_model, dst_model)
>>> trans_matrix = model_trans.train(data)
>>>
>>> result = model_trans.infer_vector(dst_model.dv[data[3].tags])

Parameters

source_lang_vec (Doc2Vec) – Source Doc2Vec model.
target_lang_vec (Doc2Vec) – Target Doc2Vec model.
tagged_docs (list of TaggedDocument, optional.) – Documents that will be used for training, both the source language document vector and target language document vector trained on those tagged documents.
random_state ({None, int, array_like}, optional) – Seed for random state.

infer_vector(target_doc_vec)¶

Translate the target model’s document vector to the source model’s document vector

Parameters: target_doc_vec (numpy.ndarray) – Document vector from the target document, whose document are not in the source model.
Returns: Vector target_doc_vec in the source model.
Return type: numpy.ndarray

classmethod load(fname, mmap=None)¶

Load an object previously saved using save() from a file.

Parameters

fname (str) – Path to file that contains needed object.
mmap (str, optional) – Memory-map option. If the object was saved with large arrays stored separately, you can load these arrays via mmap (shared memory) using mmap=’r’. If the file being loaded is compressed (either ‘.gz’ or ‘.bz2’), then `mmap=None must be set.

See also

load(): Load object from file.

train(tagged_docs)¶

Build the translation matrix that mapping from the source model’s vector to target model’s vector

Parameters: tagged_docs (list of TaggedDocument, Documents) – that will be used for training, both the source language document vector and target language document vector trained on those tagged documents.
Returns: Translation matrix that mapping from the source model’s vector to target model’s vector.
Return type: numpy.ndarray

class gensim.models.translation_matrix.Space(matrix, index2word)¶

Bases: object

An auxiliary class for storing the the words space.

Parameters

matrix (iterable of numpy.ndarray) – Matrix that contains word-vectors.
index2word (list of str) – Words which correspond to the matrix.

classmethod build(lang_vec, lexicon=None)¶

Construct a space class for the lexicon, if it’s provided.

Parameters

lang_vec (KeyedVectors) – Model from which the vectors will be extracted.
lexicon (list of str, optional) – Words which contains in the lang_vec, if lexicon = None, the lexicon is all the lang_vec’s word.

Returns

Object that stored word-vectors

Return type

Space

normalize()¶: Normalize the word vector’s matrix.

class gensim.models.translation_matrix.TranslationMatrix(source_lang_vec, target_lang_vec, word_pairs=None, random_state=None)¶

Bases: gensim.utils.SaveLoad

Objects of this class realize the translation matrix which map the source language to the target language. The main methods are:

We map it to the other language space by computing z = Wx, then return the word whose representation is close to z.

The details use seen the notebook 3

Examples

>>> from gensim.models import KeyedVectors
>>> from gensim.test.utils import datapath
>>> en = datapath("EN.1-10.cbow1_wind5_hs0_neg10_size300_smpl1e-05.txt")
>>> it = datapath("IT.1-10.cbow1_wind5_hs0_neg10_size300_smpl1e-05.txt")
>>> model_en = KeyedVectors.load_word2vec_format(en)
>>> model_it = KeyedVectors.load_word2vec_format(it)
>>>
>>> word_pairs = [
...     ("one", "uno"), ("two", "due"), ("three", "tre"), ("four", "quattro"), ("five", "cinque"),
...     ("seven", "sette"), ("eight", "otto"),
...     ("dog", "cane"), ("pig", "maiale"), ("fish", "cavallo"), ("birds", "uccelli"),
...     ("apple", "mela"), ("orange", "arancione"), ("grape", "acino"), ("banana", "banana")
... ]
>>>
>>> trans_model = TranslationMatrix(model_en, model_it)
>>> trans_model.train(word_pairs)
>>> trans_model.translate(["dog", "one"], topn=3)
OrderedDict([('dog', [u'cane', u'gatto', u'cavallo']), ('one', [u'uno', u'due', u'tre'])])

References

3(1,2): https://github.com/RaRe-Technologies/gensim/blob/3.2.0/docs/notebooks/translation_matrix.ipynb

Parameters

source_lang_vec (KeyedVectors) – Word vectors for source language.
target_lang_vec (KeyedVectors) – Word vectors for target language.
word_pairs (list of (str, str), optional) – Pairs of words that will be used for training.
random_state ({None, int, array_like}, optional) – Seed for random state.

apply_transmat(words_space)¶

Map the source word vector to the target word vector using translation matrix.

Parameters: words_space (Space) – Object that constructed for those words to be translate.
Returns: Object that constructed for those mapped words.
Return type: Space

classmethod load(fname, mmap=None)¶

Load an object previously saved using save() from a file.

Parameters

fname (str) – Path to file that contains needed object.
mmap (str, optional) – Memory-map option. If the object was saved with large arrays stored separately, you can load these arrays via mmap (shared memory) using mmap=’r’. If the file being loaded is compressed (either ‘.gz’ or ‘.bz2’), then `mmap=None must be set.

Get Expert Help From The Gensim Authors

models.translation_matrix – Translation Matrix model¶

How to make translation between two set of word-vectors¶

How to make translation between two Doc2Vec models¶

`models.translation_matrix` – Translation Matrix model¶

How to make translation between two `Doc2Vec` models¶