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Fix citation of Mikolov paper #2098

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Fix citation of Mikolov paper #2098

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e41c435
Fix citation
davidchall Jun 20, 2018
9fc1ed6
Fix citation
davidchall Jun 20, 2018
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6 changes: 3 additions & 3 deletions gensim/models/phrases.py
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Original file line number Diff line number Diff line change
Expand Up @@ -621,8 +621,8 @@ def __getitem__(self, sentence):


def original_scorer(worda_count, wordb_count, bigram_count, len_vocab, min_count, corpus_word_count):
"""Calculation score, based on original `"Efficient Estimaton of Word Representations in Vector Space" by
Mikolov <https://arxiv.org/pdf/1301.3781.pdf>`_.
"""Calculation score, based on original `"Distributed Representations of Words and Phrases
and their Compositionality" by Mikolov <https://arxiv.org/pdf/1310.4546.pdf>`_.

Parameters
----------
Expand All @@ -641,7 +641,7 @@ def original_scorer(worda_count, wordb_count, bigram_count, len_vocab, min_count

Notes
-----
Formula: :math:`\\frac{(worda\_count - min\_count) * len\_vocab }{ (worda\_count * wordb\_count)}`.
Formula: :math:`\\frac{(bigram\_count - min\_count) * len\_vocab }{ (worda\_count * wordb\_count)}`.

"""
return (bigram_count - min_count) / worda_count / wordb_count * len_vocab
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10 changes: 5 additions & 5 deletions gensim/sklearn_api/phrases.py
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Expand Up @@ -35,9 +35,9 @@
class PhrasesTransformer(TransformerMixin, BaseEstimator):
"""Base Phrases module, wraps :class:`~gensim.models.phrases.Phrases`.

For more information, please have a look to `Mikolov, et. al: "Efficient Estimation of Word Representations in
Vector Space" <https://arxiv.org/pdf/1301.3781.pdf>`_ and `Gerlof Bouma: "Normalized (Pointwise) Mutual Information
in Collocation Extraction" <https://svn.spraakdata.gu.se/repos/gerlof/pub/www/Docs/npmi-pfd.pdf>`_.
For more information, please have a look to `Mikolov, et. al: "Distributed Representations of Words and Phrases and
their Compositionality" <https://arxiv.org/pdf/1310.4546.pdf>`_ and `Gerlof Bouma: "Normalized (Pointwise) Mutual
Information in Collocation Extraction" <https://svn.spraakdata.gu.se/repos/gerlof/pub/www/Docs/npmi-pfd.pdf>`_.

"""
def __init__(self, min_count=5, threshold=10.0, max_vocab_size=40000000,
Expand All @@ -63,8 +63,8 @@ def __init__(self, min_count=5, threshold=10.0, max_vocab_size=40000000,
or with a function with the expected parameter names. Two built-in scoring functions are available
by setting `scoring` to a string:

* 'default': Explained in `Mikolov, et. al: "Efficient Estimation of Word Representations
in Vector Space" <https://arxiv.org/pdf/1301.3781.pdf>`_.
* 'default': Explained in `Mikolov, et. al: "Distributed Representations of Words and Phrases
and their Compositionality" <https://arxiv.org/pdf/1310.4546.pdf>`_.
* 'npmi': Explained in `Gerlof Bouma: "Normalized (Pointwise) Mutual Information in Collocation
Extraction" <https://svn.spraakdata.gu.se/repos/gerlof/pub/www/Docs/npmi-pfd.pdf>`_.

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