I want to build a seq2seq chatbot with a pre-trained Embedding matrix. Does the pre-trained Embedding matrix, for example GoogleNews-vectors-negative300, FastText and GloVe, has the specific word vector for <EOS> and <UNK>?
The pre-trained embedding has a specific vocabulary defined. The words which are not in vocabulary are called words also called oov( out of vocabulary) words. The pre-trained embedding matrix will not provide any embedding for UNK. There are various methods to deal with the UNK words.
Ignore the UNK word
Use some random vector
Use Fasttext as pre-trained model because it solves the oov problem by constructing vector for the UNK word from n-gram vectors that constitutes a word.
If the number of UNK is low the accuracy won't get affected a lot. If the number is higher better to train embedding or use fast text.
"EOS" Token can also be taken (initialized) as a random vector.
Make sure the both random vectors are not the same.
Related
I want to experiment with different embeddings such Word2Vec, ELMo, and BERT but I'm a little confused about whether to use the word embeddings or sentence embeddings, and why. I'm using the embeddings as features input to SVM classifier.
Thank you.
Though both approaches can prove efficient for different datasets, as a rule of thumb I would advice you to use word embeddings when your input is of a few words, and sentence embeddings when your input in longer (e.g. large paragraphs).
I am working on a text generation using seq2seq model where GloVe embedding is being used. I want to use a custom Word2Vec (CBOW/Gensim) embedding in this code. Can anyone please help to use my custom embedding instead of GloVe?
def initialize_embeddings(self):
"""Reads the GloVe word-embeddings and creates embedding matrix and word to index and index to word mapping."""
# load the word embeddings
self.word2vec = {}
with open(glove_path%self.EMBEDDING_DIM, 'r') as file:
for line in file:
vectors = line.split()
self.word2vec[vectors[0]] = np.asarray(vectors[1:], dtype="float32")```
```# get the embeddings matrix
self.num_words = min(self.MAX_VOCAB_SIZE, len(self.word2idx)+1)
self.embeddings_matrix = np.zeros((self.num_words, self.EMBEDDING_DIM))
for word, idx in self.word2idx.items():
if idx <= self.num_words:
word_embeddings = self.word2vec.get(word)
if word_embeddings is not None:
self.embeddings_matrix[idx] = word_embeddings
self.idx2word = {v:k for k,v in self.word2idx.items()}
This code is for GloVe embedding which is transformed to Word2Vec. I want to load my own Word2Vec embedding.
word2vec and Glove are a techniques for producing word embeddings, i.e., for modelling text (a set of sentences) into computer-readable vectors.
While word2vec trains on the local context (neighboring words), Glove will look for words co-occurrence in a whole text or corpus, its approach is more global.
word2vec
There are two main approaches for word2vec, in which the algorithm loops through the worlds of the sentence. For each current word w it will try to predict
the neighboring words from w and its context, this is the Skip-Gram approach
w from its context, this is the CBOW approach
Hence, word2vec will produce a similar embedding for words with similar contexts, for instance a noun in singular and its plural, or two synonyms.
Glove
The main intuition underlying the Glove model is the simple observation that ratios of word-word co-occurrence probabilities have the potential for encoding some form of meaning. In other words the embeddings are based on the computation of distances between pairs of target words. The model computes the distance between two target words in a text by analyzing the co-occurence of those two target words with some other probe words (contextual words).
https://nlp.stanford.edu/projects/glove/
For example, consider the co-occurrence probabilities for target words "ice" and "steam" with various probe words from the vocabulary. Here are some actual probabilities from a 6 billion word corpus:
As one might expect, "ice" co-occurs more frequently with "solid" than it does with "gas", whereas "steam" co-occurs more frequently with "gas" than it does with "solid". Both words co-occur with their shared property "water" frequently, and both co-occur with the unrelated word "fashion" infrequently. Only in the ratio of probabilities does noise from non-discriminative words like "water" and "fashion" cancel out, so that large values (much greater than 1) correlate well with properties specific to "ice", and small values (much less than 1) correlate well with properties specific of "steam". In this way, the ratio of probabilities encodes some crude form of meaning associated with the abstract concept of thermodynamic phase.
Also, Glove is very good at analogy, and performs well on the word2vec dataset.
We generally compare similarity between word embeddings with cosine similarity, but this only takes into account the angle between the vectors, not the norm. With word2vec, the norm of the vector decreases as the word is used in more varied contexts. So, stopwords are close to 0 and very unique, high meaning words tend to be large vectors. BERT is context sensitive, so this explanation doesn't entirely cover BERT embeddings. Does anyone have any idea what the significance of vector magnitude could be with BERT?
I don't think there is any difference in relation to cosine similarity or norm of a vector, between BERT and other embeddings like GloVE or Word2Vec. Its just that BERT is context dependent embedding so provide different embeddings of a word for different context.
so I wanna ask you which is the best tool used to prepare my text to deep learning?
What is the difference between Word2Vec, Glove, Keras, LSA...
You should use a pre-trained embedding to represent the sentence into a vector or a matrix. There are a lot of sources where you can find pre-trained embeddings that use different dataset (for instance all the Wikipedia) to train their models. These models can have different length, but normally each word is represented with 100 or 300 dimensions.
Pre-trained embeddings
Pre-trained embeddings 2
I have word embeddings for 10 million words which were trained on a huge corpus. Now I want to produce word embeddings for out of vocabulary words. Can I design some char RNN to use these word embeddings and generate embeddings for out of vocab words? Or is there any other I can get embeddings for OOV words?
FastText is capable of producing word embeddings for OOV but it does not have distributed way of training or GPU implementation, so in my case, it could take almost 3 months to finish training. Any suggestions on this?