将文本文件中的单词列表转换为Word向量

Question

I have a text file with million of rows which I wanted to convert into word vectors and later on I can compare these vectors with a search keyword and see which all texts are closer to the search keyword.

My Dilemma is all the training files that I have seen for the Word2vec are in the form of paragraphs so that each word has some contextual meaning within that file. Now my file here is independent and contains different keywords in each row.

My question is whether is it possible to create word embedding using this text file or not, if not then what's the best approach for searching a matching search keyword in this million of texts

**My File Structure: **

Walmart
Home Depot
Home Depot
Sears
Walmart
Sams Club
GreenMile
Walgreen

Expected

search Text : 'WAL'

Result from My File:

WALGREEN
WALMART
WALMART

Answer 1

Embeddings

Lets step back and understand what is word2vec. Word2vec (like Glove, FastText etc) is a way to represent words as vectors. ML models don't understand words they only understand numbers so when we are dealing with words we would want to convert them into numbers (vectors). One-hot encoding is one naive way of encoding words as vectors. But for a large vocabulary one-hot encoding become too long. Also there is no semantic relationship between one-hot encoded word.

With DL came the distributed representation of words (called word embeddings). One important property of these word embeddings is that the vector distance between related words is small compared to the distance between unrelated words. ie distance(apple,orange) < distance(apple,cat)

So how are these embedding model trained ? The embedding models are trained on (very) huge corpus of text. When you have huge corpus of text the model will learn that the apple are orange are used (many times) in same context. It will learn that the apple and orange are related. So to train a good embedding model you need huge corpus of text (not independent words because independent words have no context).

However, one rarely trains a word embedding model form scratch because good embedding model are available in open source. However, if your text is domain specific (say medical) then you do a transfer learning on openly available word embeddings.

Out of vocabulary (OOV) words

Word embedding like word2vec and Glove cannot return an embedding for OOV words. However the embeddings like FastText (thanks to @gojom for pointing it out) handle OOV words by breaking them into n-grams of chars and build a vector by summing up subword vectors that would make up the word.

Problem

Coming to your problem,

Case 1: lets say the user enters a word WAL , first of all it is not a valid English word so it will not be in vocabulary and it is hard to mind a meaning full vector to it. Embeddings like FastText handling them by breaking it into n-grams. This approach gives good embeddings for misspelled words or slang.

Case 2: Lets say the user enters a word WALL and if you plan to use vector similarly to find closest word it will never be close to Walmart because semantically they are not related. It will rather be close to words like window, paint, door .

Conclusion

If your search is for semantically similar words, then solution using vector embeddings will be good. On the other hand, if your search is based on lexicons then vectors embeddings will be of no help.

Answer 2

If you wanted to find walmart from a fragment like wal , you'd more likely use something like:

• a substring or prefix search through all entries; or
• a reverse-index-of-character-n-grams; or
• some sort of edit-distance calculated against all entries or a subset of likely candidates

That is, from your example desired output, this is not really a job for word-vectors, even though some algorithms, like FastText, will be able to provide rough vectors for word-fragments based on their overlap with trained words.

If in fact you want to find similar stores, word-vectors might theoretically be useful. But the problem given your example input is that such word-vector algorithms require examples of tokens used in context , from sequences-of-tokens that co-appear in natural-language-like relationships. And you want lots of data featuring varied examples-in-context, to capture subtle gradations of mutual relationships.

While your existing single-column of short entity-names (stores) can't provide that, maybe you have something applicable elsewhere, if you have richer data sources. Some ideas might be:

• lists of stores visited by a single customer
• lists of stores carrying the same product/UPC
• text from a much larger corpus (such as web-crawled text, or maybe Wikipedia) in which there are sufficient in-context usages of each store-name. (You'd just throw out all the other words created from such training - but the vectors for your tokens-of-interest might still be of use in your domain.)