Character-Word Embeddings from lm_1b in Keras

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I would like to use some pre-trained word embeddings in a Keras NN model, which have been published by Google in a very well known article. They have provided the code to train a new model, as well as the embeddings here.

However, it is not clear from the documentation how to retrieve an embedding vector from a given string of characters (word) from a simple python function call. Much of the documentation seems to center on dumping vectors to a file for an entire sentence presumably for sentimental analysis.

So far, I have seen that you can feed in pretrained embeddings with the following syntax:

embedding_layer = Embedding(number_of_words??,
                            out_dim=128??,
                            weights=[pre_trained_matrix_here],
                            input_length=60??,
                            trainable=False)

However, converting the different files and their structures to pre_trained_matrix_here is not quite clear to me.

They have several softmax outputs, so I am uncertain which one would belong - and furthermore how to align the words in my input to the dictionary of words for which they have.

Is there a simple manner to use these word/char embeddings in keras and/or to construct the character/word embedding portion of the model in keras such that further layers may be added for other NLP tasks?

language-model word-embedding keras machine-learning nlp
2 Answers

The Embedding layer only picks up embeddings (columns of the weight matrix) for integer indices of input words, it does not know anything about the strings. This means you need to first convert your input sequence of words to a sequence of indices using the same vocabulary as was used in the model you take the embeddings from.

user_56523130

For NLP applications that are related to word or text encoding I would use CountVectorizer or TfidfVectorizer. Both are announced and described in a brief way for Python in the following reference: http://www.bogotobogo.com/python/scikit-learn/files/Python_Machine_Learning_Sebastian_Raschka.pdf

CounterVectorizer can be used for simple application as a SPAM-HAM detector, while TfidfVectorizer gives a deeper insight of how relevant are each term (word) in terms of their frequency in the document and the number of documents in which appears this result in an interesting metric of how discriminant are the terms considered. This text feature extractors may consider a stop-word removal and lemmatization to boost features representations.

user_49176480
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