There are many options, much better that this one, but doc2vec, the extension of word2vec for sentences and documents will work for you https://radimrehurek.com/gensim/models/doc2vec.html
All the best!
El vie., 19 oct. 2018 a las 10:10, Alexander Osherenko (<osherenko at gmx.de>) escribió:
> Thanks, Mohammad. Unfortunately, I looking for a geometric representation
> of phrases, not of words.
> Best, Alexander
> Am Fr., 19. Okt. 2018 um 11:01 Uhr schrieb Mohammad Akbari <
> akbari.ma at gmail.com>:
>> Hello Alexander,
>> Word embedding models, such as word2vec, and glove, are common
>> approaches; where words represented with a numerical vector (
>> https://code.google.com/archive/p/word2vec/). When you have word
>> embedding, you can do geometric computations based other vectors. A common
>> approach is to compute the average embedding of all words in a phrase; You
>> can check fasttext for this purpose.
>> On 19 Oct 2018, at 09:41, Alexander Osherenko <osherenko at gmx.de> wrote:
>> I wonder if it is possible to represent NL phrases geometrically, for
>> example, to compare their similarity. For example, the phrase "Hey man,
>> that chick *is such a catch!*" and more formal "..., this girl is
>> pretty!" should be represented geometrically nearby because they are
>> semantically similar.
>> I am aware of LSA vectors that represent particular words and similarity
>> could be evaluated as a distance between these word vectors in the LSA
>> space. However, the LSA approach only works for individual words and no
>> phrases and it is IMHO too numerical because it doesn't consider
>> semantics of participating words.
>> Best, Alexander
>> Alexander Osherenko, Dr. rer. nat.
>> Senior HCI architect
>> Founder and R&D
>> Socioware Development <http://www.socioware.de/osherenko_page.html>
>> Profile: ResearchGate
>> Implementing Social Smart Environments with a Large Number of Believable
>> Inhabitants in the Context of Globalization
>> <https://www.researchgate.net/publication/327425719_Implementing_Social_Smart_Environments_with_a_Large_Number_of_Believable_Inhabitants_in_the_Context_of_Globalization> at
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