Generating Similar Words (or Synonyms) with Word Embeddings (Word2Vec) Unicorn Meta Zoo #1: Why another podcast? Announcing the arrival of Valued Associate #679: Cesar Manara 2019 Moderator Election Q&A - Questionnaire 2019 Community Moderator Election ResultsHow to handle Memory issues in training Word Embeddings on Large Datasets?How to overcome training example's different lengths when working with Word Embeddings (word2vec)Understanding Word EmbeddingsK-means clustering of word embedding gives strange resultsWhy would you use word embeddings to find similar words?Learning word embeddings using RNNHow to count number of word embeddings in Gensim Word2Vec modelPossible reasons for word2vec learning context words as most similar rather than words in similar contextsWord embeddings and punctuation symbolsDocument parsing modeling and approach?
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Generating Similar Words (or Synonyms) with Word Embeddings (Word2Vec)
Unicorn Meta Zoo #1: Why another podcast?
Announcing the arrival of Valued Associate #679: Cesar Manara
2019 Moderator Election Q&A - Questionnaire
2019 Community Moderator Election ResultsHow to handle Memory issues in training Word Embeddings on Large Datasets?How to overcome training example's different lengths when working with Word Embeddings (word2vec)Understanding Word EmbeddingsK-means clustering of word embedding gives strange resultsWhy would you use word embeddings to find similar words?Learning word embeddings using RNNHow to count number of word embeddings in Gensim Word2Vec modelPossible reasons for word2vec learning context words as most similar rather than words in similar contextsWord embeddings and punctuation symbolsDocument parsing modeling and approach?
$begingroup$
We have a search engine, and when users type in Tacos, we also want to search for similar words, such as Chilis or Burritos.
However, it is also possible that the user search with multiple keywords. Such as Tacos Mexican Restaurants, and we also want to find similar word such as Chilis or Burritos.
What we do is to add all the vectors together for each word. This sometimes works, but with more keywords the vectors tend to be in a place where there are no neighbors.
Is there an approach where we can use not only one word, but multiple word, and still gives us similar results? We are using pre-trained glove vectors from Stanford, would it help if we train on articles that are food related, and specifically use that type of word embeddings for this task?
word2vec word-embeddings
asked Apr 5 at 23:41
user1157751user1157751
2201416
$endgroup$
add a comment |
$begingroup$
We have a search engine, and when users type in Tacos, we also want to search for similar words, such as Chilis or Burritos.
However, it is also possible that the user search with multiple keywords. Such as Tacos Mexican Restaurants, and we also want to find similar word such as Chilis or Burritos.
What we do is to add all the vectors together for each word. This sometimes works, but with more keywords the vectors tend to be in a place where there are no neighbors.
Is there an approach where we can use not only one word, but multiple word, and still gives us similar results? We are using pre-trained glove vectors from Stanford, would it help if we train on articles that are food related, and specifically use that type of word embeddings for this task?
word2vec word-embeddings
asked Apr 5 at 23:41
user1157751user1157751
2201416
$endgroup$
add a comment |
$begingroup$
We have a search engine, and when users type in Tacos, we also want to search for similar words, such as Chilis or Burritos.
However, it is also possible that the user search with multiple keywords. Such as Tacos Mexican Restaurants, and we also want to find similar word such as Chilis or Burritos.
What we do is to add all the vectors together for each word. This sometimes works, but with more keywords the vectors tend to be in a place where there are no neighbors.
Is there an approach where we can use not only one word, but multiple word, and still gives us similar results? We are using pre-trained glove vectors from Stanford, would it help if we train on articles that are food related, and specifically use that type of word embeddings for this task?
word2vec word-embeddings
asked Apr 5 at 23:41
user1157751user1157751
2201416
$endgroup$
We have a search engine, and when users type in Tacos, we also want to search for similar words, such as Chilis or Burritos.
However, it is also possible that the user search with multiple keywords. Such as Tacos Mexican Restaurants, and we also want to find similar word such as Chilis or Burritos.
What we do is to add all the vectors together for each word. This sometimes works, but with more keywords the vectors tend to be in a place where there are no neighbors.
Is there an approach where we can use not only one word, but multiple word, and still gives us similar results? We are using pre-trained glove vectors from Stanford, would it help if we train on articles that are food related, and specifically use that type of word embeddings for this task?
word2vec word-embeddings
word2vec word-embeddings
asked Apr 5 at 23:41
user1157751user1157751
2201416
asked Apr 5 at 23:41
user1157751user1157751
2201416
asked Apr 5 at 23:41
user1157751user1157751
2201416
asked Apr 5 at 23:41
user1157751user1157751
2201416
asked Apr 5 at 23:41
user1157751user1157751
2201416
2201416
add a comment |
add a comment |
1 Answer
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$begingroup$
Word Mover’s Distance (WMD) is an algorithm for finding the minimum distance between multiple embedded words.
The WMD distance measures the dissimilarity between two text documents as the minimum amount of distance that the embedded words of one document need to "travel" to reach the embedded words of another document.
For example:
Source: "From Word Embeddings To Document Distances" Paper
In your problem, it will allow you to find that "Tacos Mexican Restaurants" is similar to "Burritos Taqueria" even though they share no common string literals.
answered Apr 6 at 16:46
Brian SpieringBrian Spiering
4,3531129
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1 Answer
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1 Answer
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$begingroup$
Word Mover’s Distance (WMD) is an algorithm for finding the minimum distance between multiple embedded words.
The WMD distance measures the dissimilarity between two text documents as the minimum amount of distance that the embedded words of one document need to "travel" to reach the embedded words of another document.
For example:
Source: "From Word Embeddings To Document Distances" Paper
In your problem, it will allow you to find that "Tacos Mexican Restaurants" is similar to "Burritos Taqueria" even though they share no common string literals.
answered Apr 6 at 16:46
Brian SpieringBrian Spiering
4,3531129
$endgroup$
add a comment |
$begingroup$
Word Mover’s Distance (WMD) is an algorithm for finding the minimum distance between multiple embedded words.
The WMD distance measures the dissimilarity between two text documents as the minimum amount of distance that the embedded words of one document need to "travel" to reach the embedded words of another document.
For example:
Source: "From Word Embeddings To Document Distances" Paper
In your problem, it will allow you to find that "Tacos Mexican Restaurants" is similar to "Burritos Taqueria" even though they share no common string literals.
answered Apr 6 at 16:46
Brian SpieringBrian Spiering
4,3531129
$endgroup$
add a comment |
$begingroup$
Word Mover’s Distance (WMD) is an algorithm for finding the minimum distance between multiple embedded words.
The WMD distance measures the dissimilarity between two text documents as the minimum amount of distance that the embedded words of one document need to "travel" to reach the embedded words of another document.
For example:
Source: "From Word Embeddings To Document Distances" Paper
In your problem, it will allow you to find that "Tacos Mexican Restaurants" is similar to "Burritos Taqueria" even though they share no common string literals.
answered Apr 6 at 16:46
Brian SpieringBrian Spiering
4,3531129
$endgroup$
Word Mover’s Distance (WMD) is an algorithm for finding the minimum distance between multiple embedded words.
The WMD distance measures the dissimilarity between two text documents as the minimum amount of distance that the embedded words of one document need to "travel" to reach the embedded words of another document.
For example:
Source: "From Word Embeddings To Document Distances" Paper
In your problem, it will allow you to find that "Tacos Mexican Restaurants" is similar to "Burritos Taqueria" even though they share no common string literals.
answered Apr 6 at 16:46
Brian SpieringBrian Spiering
4,3531129
answered Apr 6 at 16:46
Brian SpieringBrian Spiering
4,3531129
answered Apr 6 at 16:46
Brian SpieringBrian Spiering
4,3531129
answered Apr 6 at 16:46
Brian SpieringBrian Spiering
4,3531129
4,3531129
add a comment |
add a comment |
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