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Summary distributed representations_words_phrases
1. A Summary for Distributed Representations of
Words and Phrases and Their Compositionality
Xiangnan YUE
xiangnanyue@gmail.com
Ingénieur Civil @Mines ParisTech
M2 Data Science @Ecole Polytechnique
2. Road Map
- Part 1 - Learning Word2Vec
- Baseline Skip-gram model
- Hierarchical softmax
- Negative sampling
- Sub-sampling trick
- Part 2 - Learning Phrase
- Part 3 - Empirical Results
- Part 4 - Additive Compositionality
- Part 5 - Conclusion
3. Background
This paper follows the paper [1] Efficient estimation of word representations in
vector space in 2013.
This paper proposed some important improvements for the Skip-gram model,
which is widely used for obtaining the famous “word vectors”. [1] proposed the
CBOW (Continuous Bag-of-words Model) and Skip-gram, using a 3-layer neural
network to train the word-vectors.
While CBOW is entering the context to predict the center, Skip-gram is entering
the center word to predict the context.
5. The principle for the baseline Skip-gram
- Inputting a centered word, the model will be able to
give us the conditional probabilities of surrounding
words. The goal is to get the hidden layer “word
vectors” by maximizing the likelyhood function.
- The objective function is to maximize a log-
likelyhood function :
- No activation function for the hidden layer, and the
output neuron used softmax function.
- The parameters of the hidden layer are trained as
the “word vectors”. It is the matrix of shape (m, n),
where m is the total number of words, and n is the
feature number (vector length)
Figure 1. Architecture of a basic skip-gram, the graph
referred from
http://mccormickml.com/2016/04/19/word2vec-tutorial-
the-skip-gram-model/
6. Remarks for Baseline Skip-gram
- The objective function is under the Naive Bayesian
assumption that the features w(t+j) and w(t+i) are independent knowing w(t)
so that p(w(t+j) | w(t)) can be separated by log(ᐧ) operation.
- The parameters for the hidden layer are too large to be trained. In the
following formula, for any input word vector , we still have to calculate the
inner product with every output vector (between the hidden layer and
the softmax) to get the output probability distribution for each word wo. The
parameter matrix between the output and the hidden layer is of the same size
of our word-vector matrix (m, n). It’s not practical when the vocabulary table
size m is too large.
7. - Besides, in order to train such a large volume of parameters, training data
volume also has to be large -- much slower as the running time is proportional
to the multiplication of the two volumes.
- Therefore, other extensions of Skip-gram were proposed, such as the
Hierarchical Softmax and the Negative Sampling (one contribution of this
paper).
- Stochastic Gradient Descent was proposed to train the Skip-gram.
Remarks for Baseline Skip-gram
9. In order to reduce the complexity of calculating the weights of output layer.
Hierarchical Softmax (HS) was used in the Skip-gram model. HS was firstly
proposed by [2] Morin and Bengio (2005).
We will introduce the data structure used in HS: Huffman Tree / Coding. Huffman
tree assigns short codes (path) to the most frequent words, and thus the random
walk on the tree will find these words quickly.
The Hierarchical Softmax
10. Huffman Tree
Huffman tree was initially proposed for lossless
data compression. It is constructed by the
frequency table of words in the corpus. It’s a
method of bottom-up.
Words are positioned as the leaf (prefix coding).
The most often appeared words are close to the
root and the most infrequent words are close to
the bottom.
The decoding path starts from the root and stops
when word w is found (reaches the leaf).
Figure 2. Huffman Tree : an
example. Graph referred from :
https://en.wikipedia.org/wiki/Huffman_coding
11. Huffman Tree
Here we use |w| to represent the total number of words, it’s the same as the
number of leafs of Huffman Tree, and |f| to represent the number of features on
the hidden layer, and denotes sigmoid function
Huffman Tree reduces the computation complexity on the output layer from
to approximate for one input sample ! The softmax
function is replaced by:
This equation explains the process: we first choose ch() to be fixed as the left child
node. Then for any chosen w, if at node j the path goes to left, we take ,
otherwise take (1 - ). The path from root to w leads to a
maximum likelyhood problem. By backpropogation, the parametres are updated.
12. The number of parameters rest the same yet a binary Huffman Tree reduced the
computation volume to an acceptable level.
Different methods for constructing the tree structure can be found by [3] Mnih and
Hinton (2009).
HS was proposed in paper [1] a priori and the performance is not satisfying. This
paper compared the results of Hierarchical Softmax with Negative Sampling, and
also Noise Contrastive Estimation - NCE proposed by [4] Gutmann and
Hyvarinen (2012), together with the subsampling trick and using phrase as
training samples.
Huffman Tree
14. Negative Sampling Method
The principle is that each sample adjusts only a small part of the parametres. The
idea is selecting a few negative samples (those which are not around the input
centered word) and only updating the output layer weight of the selected negative
words and the positive word.
Remark:
- It’s a similar way to dropout layer in deep neural network, yet the dropout
layer is between the hidden layers, instead of removing the output units.
- In the hidden layer, the number of updated weights are not changed -- only
the feature weights of input words are to be changed.
15. Negative Sampling Method
Ways to select Negative Samples: Use the uni-gram distribution Pn(w) to select
the negative words. According to the paper, in the objective function,
is replaced by the following function
a ¾ power value is used with the uni-gram percentage. This value is empirical.
Note that is the sigmoid function: ,
thus the first term above is the positive word’s log probability and the second term
is the negative words’ non-observe log probability.
17. Subsampling Heuristic
Sub-sampling is important because some words are much more frequent than
others. Therefore when the sampling window size is large enough, some words
appear quite often in sampling windows yet doesn’t reflect any information (the
same goes for a variable with probability 1 doesn’t give any information as
entropy).
Heuristically, by removing the appearing rate of the same word while conserving
the ranking in the whole documents will even improve the final results. The
method used in this paper is a sub-sampling by a probability of saving which is of
the order .
The discard Probability : , t is called the
subsampling rate, and f(w) is the frequency of the word w in the corpus.
19. Use Phrases
Another important contribution is to use the phrases (or word pairs) rather than the
words in their training samples. Though this can be regarded as a prepared step,
it’s of great importance, given that:
- The samples volume is greatly reduced.
- The meaning of compositioned phrases are saved.
The scores for co-appearance are calculated simply by 2-gram / 1-gram. When
the score is large enough, two words are combined into one single phrase.
The whole training data set is passing 3-4 times this preprocessing algorithm. In
the paper, the obtained phrases’ vector quality is tested by another analogical
reasoning task (72% accuracy).
21. Analogical Reasoning Task
An example for analogical reasoning is
as left. Given three phrases, to how
much degree can the fourth phrase be
inferred from the first three phrase-
vectors. By using cosine distance
between the vectors, the closest answer
is selected and compared with the true
answer.
This test method is used also in [1].
Figure 3. Examples for Analogical Reasoning test : In
each cell, given the first three words, test the accuracy
of calculating the fourth.
22. Empirical Results (words, no phrase learning)
Using Analogical Reasoning Task, the empirical results is listed in this paper.
- Negative Sampling outperforms the Hierarchical Softmax, and slightly
better than the Noise Contrastive Estimation.
- The subsampling speeds up the algorithm and even got better results for
Semantic and Total accuracy.
dimensionality = 300;
context size = 5;
discard words with
frequence < 5;
k = 5, 15 (the number
of negative words);
# of words = billions
23. Empirical Results (using phrases)
Using Analogical Reasoning Task, the empirical results is listed in this paper.
- Using subsampling, Hierarchical Softmax outperforms the other methods !
Yet when no subsampling, HS got the worst accuracy.
- As using phrases largely reduced the total number training samples, more
training data is needed (to be continued...)
dimensionality = 300;
context size = 5;
discard words with
frequence < 5;
k = 5, 15 (the number
of negative words);
24. Empirical Results (using phrases)
The paper claims that when using 33 billion words, dimensionality = 1000, and
the entire sentence as context size, this resulted in an accuracy of 72%. The
best performed model is still HS + subsampling.
26. Additive Compositionality
Additive Compositionality is an interesting property of word vector. It shows that a
non-obvious degree of language understanding can be obtained by using basic
operations (sum here) on the word representation. That is something more than
analogical reasoning.
The author gave an intuitive explanation as follows (the product of distributions.)
28. Conclusion
This paper mainly has three contributions:
- Using common word pairs (phrase representations) to replace words in the
training model.
- Subsampling the frequent words to largely decrease training time and get
better performance for infrequent words.
- Proposing Negative Sampling
- Comparing different methods’ combination and trained a best model (up-to-
then) on a huge (30 billions words) data set.
- Addressing the additive compositionality of word vector.
The works of this paper can be further used in CBOW in [1], and the code is given
at https://code.google.com/archive/p/word2vec/
29. Referrence
[1] Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. Efficient estimation of word
representations in vector space. ICLR Workshop, 2013.
[2] Frederic Morin and Yoshua Bengio. Hierarchical probabilistic neural network language model. In Pro-
ceedings of the international workshop on artificial intelligence and statistics, pages 246–252, 2005.
[3] Andriy Mnih and Geoffrey E Hinton. A scalable hierarchical distributed language model. Advances in
neural information processing systems, 21:1081–1088, 2009.
[4] Michael U Gutmann and Aapo Hyva ̈rinen. Noise-contrastive estimation of unnormalized statistical
mod- els, with applications to natural image statistics. The Journal of Machine Learning Research,
13:307–361, 2012.
