Questions tagged [word2vec]

word2vec is a two layer neural network to process text. It takes words as an input and outputs a vector correspondingly. It uses a combination of Continuous Bag of Word and skipgram model implementation.

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How do I convert my dataframe column into vectors using word2vec?

I have the column of Categories of vary length in my DataFrame as shown below : Categories [Restaurants , Drinks] [Restaurants , Drinks , Rooftop] [Dinner] . . . ...
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Human readable format for clusters of word vectors

Let's say I have pretrained word2vec model and apply it to dataset consisting of article titles from "The Guardian". It seems pretty obvious that titles coming from "Science" ...
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Where the objective function of Skip-Gram Negative Sampling (SGNS) come from?

In the paper of "Neural Word Embedding as Implicit Matrix Factorization", there is an objective function for Skip-Gram Negative Sampling. I wanted to know where this formula come from
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Any research on relationship between the dimensions of a (word2Vec) space and how the human mind constructs meaning (or reality) through language?

Neuroscience is still trying to "find" how the mind (and language) somehow "works". Is there any theory linking a (low-dimensionality) embedding space (like word2Vec) to a mind (...
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Why is averaging the vectors required in word2vec?

While implementing word2vec using gensim by following few tutorials online, one thing that I couldn't understand is the reason why word vectors are averaged once the model is trained. Few example ...
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Word2vec outperforming BERT, possible?

I'm trying to solve a multilabel classification (dataset is tweet text) using a combination of BERT and CNN. As a benchmark, I'd compare it to other word embeddings, one of which is Word2vec. After ...
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Why does word2vec try to maximize the dot product of the center word vector and context?

I am learning about the maths behind word2vec from this tutorial. u are the embeddings for the center word and v for the context. It appears that this dot product is to be maximized. Why the context ...
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The reason behind using a pre-trained model?

These last month I have been studying all about word embeddings and the most known pre-trained word embeddings, Word2Vec, GloVe, FastText, etc. I have read many times how important It is to take ...
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Word2Vec: Identifying many-to-one relationships between words

Standard introductory examples in Word2Vec, like king - queen = man - woman and tokyo - japan = london - uk, involve one-to-one ...
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How to train effective domain specific word2vec models?

I'm training a word2vec model based on our custom data. Due to the size of data and the resources available to me, I'll use pyspark to train spark's built-in word2vec model. However, I'm curious to ...
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Difference between NCE-Loss and InfoNCE-Loss

I started looking into word2vec and was wondering what the connection/difference between the NCE-Loss and the infoNCE-Loss is. I get the basic idea of both. I have a hard time deriving one from ...
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Text preprocessing on corpus in pipeline before Gensim word2vec training

I have a large compressed corpus, about 30gb in .txt.gz format. In raw format it can be used as input to word2vec like this: ...
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How can I convert my predictions to text after predicting using RNN?

I'm building PoS tagger for our language. I give tokens to the words and tags using Tokenizer(). Functions for word and tag are different. ...
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why does not CBOW of word2vec include center word in context

In CBOW, each word has input vector and output vector, or context vector and self vector, the context aggregate the surrounding words, what if the context also contain the input vector of the center ...
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What is the meaning of, or explanation for, having multiple tags in a Doc2Vec model's TaggedDocuments?

I've tried reading the other answers on this topic but I'm unsure if I understand completely. For my dataset, I have a series of tagged documents, "good" or "bad." Each document ...
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How to implement co-occurance matrix in Python which only takes into account next word relationships

I've been trying to create a dictionary version of a co-occurence matrix in Python, such that it only counts instances where one word follows another in a set of sentences. I found this implementation ...
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Skipgram model theory confusion

In the output layer of a skipgram model, there are $|\text{Context}|*|\text{Vocab}|$ values. And for each context word, the values are basically the dot product of the input word representation and ...
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Word2Vector multicontext CBOW model with Adam optimization

In cbow multiword context word2vec model there are two weights matrixes $$ W,W^{'}$$ Where $W$ is $I$ -> $H$ weight matrix, and $W^{'}$ is $H$ -> $U$ weight matrix and output is just softmax ...
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Clustering together words that appear together while down weighting words that appear too often

I was wondering if I could get some help finding a good model for the problem I have. I have a data set where each observation is a set of words that go together. So for example, it could be: ...
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Word2Vec vs. Doc2Vec Word Vectors

I am doing some analysis on document similarity and was also interested in word similarity. I know that doc2vec inherits from word2vec and by default trains using word vectors which we can access. My ...
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Return the most relevant columns for a given keyword

Suppose my database has column name and description for each column of each table. Need to design an interface where a user can enter a keyword and the interface will return the most relevance columns....
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Dot product for similarity in word to vector computation in NLP

In NLP while computing word to vector we try to maximize log(P(o|c)). Where P(o|c) is probability that o is outside word, given that c is center word. Uo is word vector for outside word Vc is word ...
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Is it acceptable to append information to word embeddings?

Let's say I have my 300 dimensional word embedding trained with Word2Vec and it contains 10,000 word vectors. I have additional data on the 10,000 words in the form of a vector (10,000x1), containing ...
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Adding extra feature to word2vec embeddings for use in classification

I have a word2vec model that will transform my text into vectors, and I need to use them to classify the text. My data is essentially a timeseries of chat messages, so I think that the message ...
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Word2Vec: Why do some dimensions of an embedding have an interpretation, and why does addition/subtraction of embedding vectors work?

I'm reading about Word2Vec from this source: http://jalammar.github.io/illustrated-word2vec/. Below is the heatmap of the embeddings for various words. In the source, it's claimed that we can get an ...
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Building simple documents search engine

I'm having my first steps in the NLP and at the moment I'm looking forward to building my own documents search engine. I've already got to know with TFIDF in practical way and I've also read about ...
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Continuous Bag Of Words (CBOW) network architecture?

Looking into word2vec like embeddings I found this exercise on PyTorch's website which prompts the reader to implement a CBOW network in PyTorch. My question is about the architecture to implement ...
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Projection layer function

I am trying to understand word-vectors and was reading this paper https://arxiv.org/pdf/1301.3781.pdf This paper proposes CBOW architecture which uses projection layer. What is projection layer? I ...
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How to test the quality of a word embedding?

I have trained a word2vec model using GenSim 4. The problem is that my corpus is quite small. How can I test the quality of the word embeddings I have obtained? Is there some standard measures to do ...
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Word2Vec vs LexVec vs GloVe

I'm on a NLP project and found a resource that has the three word representations mentioned in the question name, and I am struggling to find one place where they all are explained and compared. As I ...
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Evaluate document similarity / content-based recommender system

I'm planning on building a basic content-based recommender system with word2vec and cosine similarity. The data consists of 300k documents in varying length. How do I evaluate my model if I have no ...
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Differentiate between positive and negative clusters

I have applied k-means clustering on my dataset of Amazon Alexa reviews. ...
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Difference between Word Embedding and Text Embedding

I am working on a dataset of amazon alexa reviews and wish to cluster them in positive and negative clusters. I am using Word2Vec for vectorization so wanted to know the difference between Text ...
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How does tf.random.log_uniform_candidate_sampler work while generating negative samples?

I was trying to build a simple negative sampler for a Word2Vec model using TensorFlow by following the tutorial here. From what I understand, the ...
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An algorithm for Automatic Tag Clustering [closed]

Out website dinf is somewhat like StackExchange: people are submitting small definitions of concepts. We would like to automatically assign those concepts into 'Topics'. The problem is that dinf by ...
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Does the output of the Sequence-to-Sequence encoder model exist in the same semantic space as the inputs (Word2vec)? [closed]

Does the output generated from the LSTM encoder module exist in the same semantic space as the original word vectors? If so, say for example we have a sentence and we pass it through the encoder to ...
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node2vec initial embedding

I am referring to the following implementation of node2vec: https://github.com/eliorc/node2vec Is there a way to have a "smart initialization" with node2vec, i.e., to start the algorithm ...
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Difference between CBOW and SKIP Gram word vectors

I have gone through several links but was not able to understand how CBOW and Skip Gram is trained from scratch? Any good link/blogs or books would be very helpful. ...
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Can we combine multiple K-Means Models as a single model?

I have a NLP problem statement where I use a Word2Vec embedding pre-trained model to convert key text to vectors and then on a set of terms run k-means clustering to get a final model for certain <...
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Is the magnitude of a word vector correlated with the frequency of the word in a text?

In order to find the similarity between words, cosine similarity seems to be the most common measure to use. In a conversation that I had about this topic, someone mentioned that words that mean more ...
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Initializing weights that are a pointwise product of multiple variables

In two-layer perceptrons that slide across words of text, such as word2vec and fastText, hidden layer heights may be a product of two random variables such as positional embeddings and word embeddings ...
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Cat2Vec implementation X = categorical and y = categorical

I am trying to convert categorical values (zipcodes) with Cat2Vec into a matrix which can be used as an input shape for categorical prediction of a target with binary values. After reading several ...
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How to find 'pre-requisite' relationships between sentences

I have two arrays. Each of them is consists of some sentences indicating some action. For example: ...
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426 views

Semantic similarity between two or more sentences

I need to determine how similar sentences (in meaning) are to one another. In order to do it, I have been considering an algorithm (cosine similarity) to determine the similarity between sentences. I ...
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How to determine semantic differences in NLP

I would need to determine the difference in meaning between the following two sentences: I am at home I am not at home I am at the office the first two sentences ...
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Construct word2vec (CBOW) training data from beginning of sentence

When constructing training data for CBOW, Mikolov et al. suggest using the word from the center of a context window. What is the "best" approach to capturing words at the beginning/end of a ...
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Size of data required to build instruction Embeddings

I am using different sizes of ARMv7 data to build instructions embedding using a conventional word2vec model which would output a numpy array of Embeddings that ...
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Distance to the Center of Vocabulary of Word Embeddings

Suppose I: Generate a set of word embeddings (using word2vec or similar) based on a large but specific corpus Compute the centroid of all the words in the set Find the word(s) with the smallest (say ...
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Is adding the embedded words of a sentence to represent the sentence a good approach?

I have a dataset of sentences in a non english language like : word1 word2 word3 word62 word5 word1 word2 Now i want to turn each variable length sentence to a fixed size vector to give it to my ...
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best approach to embed random length sequences of words as a fixed size vector without having a maximum length? [closed]

I have a dataset of sentences in a non-English language like: word1 word2 word3 word62 word5 word1 word2 and the length of each sentence is not fixed. Now, I want to represent each sentence as a ...

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