Word Embedding for Item Names(integer, one-hot encoding) I am looking for the way to get the similarity between two item names using integer encoding or one-hot encoding.
For example, &quot;lane connector&quot; vs. &quot;a truck crane&quot;.
I have 100,000 item names consisting of 2~3 words as above.
also, items have its size(36mm, 12M, 2400*1200...) and unit(ea, m2, m3, hr...)
I wanna make (item name, size, unit) as a vector. To do this, I need to change texts to numbers using some way. All I found is only word2vec things, but my case has no context corpus. So I don't think it is possible to learn some context from my data.
I'm not sure, if it's possible with this data set. Word2Vec is used to generate word embedding, which works on the principle of "words association" in a sentence.

So I dont think you can apply Word2Vec on this dataset which looks like doesn't have any association, except on some places where you can match(perform clustering) some parameters like:


Units
Size/dimension of the item-name


Interested to know some solution for such types of problems.