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Taken from this post:https://stats.stackexchange.com/a/245452/154812 The issue There are some issues with learning the word vectors using an "standard" neural network. In this way, the word vectors are learned while the network learns to predict the next word given a window of words (the input of the network). Predicting the next word is like predicting ...


22

Thank Abhishek. I've figure it out! Here are my experiments. 1). we plot a easy example: from gensim.models import Word2Vec from sklearn.decomposition import PCA from matplotlib import pyplot # define training data sentences = [['this', 'is', 'the', 'first', 'sentence', 'for', 'word2vec'], ['this', 'is', 'the', 'second', 'sentence'], ...


17

The answer to this question is that it depends. The primary approach is to pass in the tokenized sentences (so SentenceCorpus in your example), but depending on what your goal is and what the corpus is you're looking at, you might want to instead use the entire article to learn the embeddings. This is something you might not know ahead of time -- so you'll ...


16

Word2vec does not capture similarity based on antonyms and synonyms. Word2vec would give a higher similarity if the two words have the similar context. Eg The weather in California was _____ . The blank could be filled by both hot and cold hence the similarity would be higher. This concept is called Paradigmatic relations. If you are interested to capture ...


16

Word embeddings are trained by substitutability, not similarity. If you consider a sentence like "This food is unflavored." Then a good substitute word would be "flavored", and the sentence will still be "correct". In many cases, substitutability arises from similarity (crunchy, crispy) but it does also arise from opposites. You may consider "king" and "...


15

One-Hot Encoding is a general method that can vectorize any categorical features. It is simple and fast to create and update the vectorization, just add a new entry in the vector with a one for each new category. However, that speed and simplicity also leads to the "curse of dimensionality" by creating a new dimension for each category. Embedding ...


12

Word2vec works in two models CBOW and skip-gram. Let's take CBOW model, as your question goes in the same way that predict the target word, given the surrounding words. Fundamentally, the model develops input and output weight matrices, which depends upon the input context words and output target word with the help of a hidden layer. Thus back-propagation ...


12

Both are possible. You can give every document a unique ID (such as a sequential serial number) as a doctag, or a shared string doctag representing something else about it, or both at the same time. The TaggedDocument constructor takes a list of tags. (If you happen to limit yourself to to plain ints ascending from 0, the Doc2Vec model will use those as ...


12

Actually they are 3 different things (embedding layer, word2vec, autoencoder), though they can be used to solve similar problems. (i.e. dense representation of data) Autoencoder is a type of neural network where the inputs and outputs are the same but in the hidden layer the dimensionality is reduced in order to get a more dense representation of the data. ...


11

doc2vec model gets its algorithm from word2vec. In word2vec there is no need to label the words, because every word has their own semantic meaning in the vocabulary. But in case of doc2vec, there is a need to specify that how many number of words or sentences convey a semantic meaning, so that the algorithm could identify it as a single entity. For this ...


11

In paragraph vector, the vector tries to grasp the semantic meaning of all the words in the context by placing the vector itself in each and every context. Thus finally, the paragraph vector contains the semantic meaning of all the words in the context trained. When we compare this to word2vec, each word in word2vec preserves its own semantic meaning. Thus ...


11

The original paper does a lot of hand-waving on the implementation of inference step and is not clear. So your confusion is justified. I'll explain at high level below. I'm assuming only PV-DBOW model. Training Phase In this model, we forget word ordering information and setup a very simple neural network. Represent all input document tags in a vocabulary ...


11

BERT and ELMo are recent advances in the field. However, there is a fine but major distinction between them and the typical task of word-sense disambiguation: word2vec (and similar algorithms including GloVe and FastText) are distinguished by providing knowledge about the constituents of the language. They provide semantic knowledge, typical about word types ...


10

1- The number of features: In terms of neural network model it represents the number of neurons in the projection(hidden) layer. As the projection layer is built upon distributional hypothesis, numerical vector for each word signifies it's relation with its context words. These features are learnt by the neural network as this is unsupervised method. Each ...


10

You have a few options here. Of these, I think 1 will be the easiest to implement, as it's a standard language model with an alignment term added to the loss. I'd recommend 2a if you think you have the time, as I imagine its performance might be much better. 1 Use existing corpus to learn embeddings for new words You can do this as you describe, though I ...


9

Every algorithm that deals with text data has a vocabulary. In the case of word2vec, the vocabulary is comprised of all words in the input corpus, or at least those above the minimum-frequency threshold. Algorithms tend to ignore words that are outside their vocabulary. However there are ways to reframe your problem such that there are essentially no Out-Of-...


9

In Text Analytic Tools for Semantic Similarity, they developed a algorithm in order to find the similarity between 2 sentences. But if you read closely, they find the similarity of the word in a matrix and sum together to find out the similarity between sentences. So, it might be a shot to check word similarity. Also in SimLex-999: Evaluating Semantic ...


9

The ratio of vocabulary vs embedding length to determine the size of other layers in a neural network doesn't really matter. Word embeddings are always around 100 and 300 in length, longer embedding vectors don't add enough information and smaller ones don't represent the semantics well enough. What matters more is the network architecture, the algorithm(s) ...


9

At theoretical level, the embedding layer is a linear layer, there is not any difference at all. However, in practice, if you are building a deep learning software, you have to make a difference among them. This is because it does not make sense to apply an embedding layer using traditional matrix multiplication, as the input matrix is very sparse. For this ...


9

Yep, you're right! As you know, it's difficult for machine learning models to use natural language directly, so it helps to transform words into some meaningful numeric representation. This process is called word embedding, and finding word embeddings is the task of the keras Embedding layer. Ideally, word embeddings will be semantically meaningful, so ...


8

Implementations These are all full scripts using Tensorflow, but just using Tensorflow does not ensure quality. Bad WangZ's implementation looks complete from a brief glance, although it only offers Adagrad and Stochastic Gradient Descent, two of the least reliable gradient descents. You really want minibatch gradient descent, which will predictably converge ...


8

Both the answers are wrong. An embedding layer is a trainable layer that contains 1 embedding matrix, which is two dimensional, in one axis the number of unique values the categorical input can take (for example 26 in the case of lower case alphabet) and on the other axis the dimensionality of your embedding space. The role of the embedding layer is to map a ...


8

You need to be careful with the assumptions you make about the doc2vec implementation. Here are some useful concepts: Word2vec has two different model implementations (Skip-gram and Continuous-bag-of-words) and Doc2vec has analogous PV-DM and CBOW models: Word2vec Continuous-bag-of-words (CBOW) Skip-gram Doc2vec (Paragraph Vector) Distributed Memory (PV-...


8

Might not be the answer you are seeking, but I'll still have a go: First, quick review of word2Vec, assume we are using skip gram. A typical Word2Vec train-able model consists of 1 input layer (for example, 10 000 long one-hot vector), a hidden layer (for example 300 neurons), an output (10 000 long one-hot vector) Input: 10 000 Hidden: 300 Output: 10 ...


8

A similar question was asked here. This Google Developers blog post says: Well, the following "formula" provides a general rule of thumb about the number of embedding dimensions: embedding_dimensions = number_of_categories**0.25 That is, the embedding vector dimension should be the 4th root of the number of categories. Interestingly, the ...


8

You're asking two questions here. Does this mean the magnitude of the vectors is irrelevant? Yes. Cosine distance is $ D_{cos} = \frac{A \cdot B}{\|A\|\|B\|} $, which just comes from the definition of inner product, $A \cdot B = \|A\|\|B\|\cos\theta$. Why is the cosine distance used? Or, to think of it another way, why is the answer to (1) a desirable ...


7

Maybe I'm having trouble formulating the inherent difference between NLP and NLU, when do we draw the line between the two? There is a confusion here: NLP is the whole domain of AI which deals with natural language. It includes virtually any task related to processing language data (usually mostly written data, but that's not the point). Topic modeling is ...


7

I will take as reference fairseq's implementation of the Transformer model. With this assumption: In the transformer, masks are used for two purposes: Padding: in the multi-head attention, the padding tokens are explicitly ignored by masking them. This corresponds to parameter key_padding_mask. Self-attention causality: in the multi-head attention blocks ...


7

BERT does not provide word-level representations, but subword representations. You may want to combine the vectors of all subwords of the same word (e.g. by averaging them), but that is up to you, BERT only gives you the subword vectors. Subwords are used for representing both the input text and the output tokens. When an unseen word is presented to BERT, it ...


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