Questions tagged [doc2vec]

Doc2vec (aka paragraph2vec, aka sentence embeddings) modifies the word2vec algorithm to unsupervised learning of continuous representations for larger blocks of text, such as sentences, paragraphs or entire documents.

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How are weights adjusted iteratively in Doc2Vec neural network and how are results from different word predictions combined?

For those unfamiliar, Doc2Vec is referred to as a 'simple' neural network with one hidden layer that is built very similar to word2vec. As I understand it (in dbow, the implementation I am using), the ...
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How does Doc2Vec handle documents where the length is less than the window size?

I am using doc2vec to vectorise documents that average in length 10 words (PV-DBOW implementation of the algorithm). I am wondering how doc2vec handles cases where the number of words in a document is ...
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How do Doc2Vec PV-DBOW iterations/epoch's work?

Please correct me if I'm wrong but as I understand it, one iteration (?) of doc2vec takes each document and predicts a series of randomly sampled context words individually, feeding the errors from ...
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Considering weights right of the embeddings layer aren't used in Doc2Vec/Word2Vec, is the informative capacity of the embeddings not strongly reduced?

In an extreme (and probably impossible) example, could you not end up with all the power for the prediction being contained in the weights to the right of the embeddings layer?...and thus the matrix ...
osckt's user avatar
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Normalizing Topic Vectors in Top2vec

I am trying to understand how Top2Vec works. I have some questions about the code that I could not find an answer for in the paper. A summary of what the algorithm does is that it: embeds words and ...
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Using doc2vec embeddings as model input our perhaps similarity comparison? [closed]

Doc2vec is an extension of word2vec, which creates vector representations of documents. One can use this representations as input to some classifier/regression(Logistic Regression, XGboost, LightGBM .....
Borut Flis's user avatar
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Doc2vec Corpus Size Recommendation

I'm trying to make a semantic search engine with Doc2Vec where you query the model a document and it returns N most similar documents from its training corpus. I'm having trouble pushing accuracy past ...
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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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NLP for customer reviews and summaries

I'm trying to develop a model in R that will compare a customer review with a summary of that review that is completed by an employee. The purpose is to ensure that the employee is accurately tagging ...
marigato's user avatar
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Generating Sentence Vectors from Word2Vec

I know that I can use doc2Wec and other resources to get sentence vectors. But I am very curious to generate sentence vectors using Word2Vec. I read lot of materials and found that Averaging the ...
chaitanya's user avatar
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Understanding Object2Vec

AWS released an interesting feature as part of the SageMaker service called Object2Vec that lets you make an embedding for search out of pretty much anything: documents, users, products, ...
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What to make of high R-squared and non-significant p-value of a linear model?

I am using doc2vec to produce $\mathbb{R}^{50}$ vector representations of short bits of text. I am then using those vectors in a linear model to predict a continuous outcome variable. The R^2 is .25 ...
Ashish's user avatar
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High Precision and low recall score for TF-IDF when using KNN algorithm

I have twitter data which is labelled with the sentiment(Postive, Negative, Neutral) and I have evaluated the performance of Tf-Idf and Doc2Vec feature extractor using the KNN algorithm and logistic ...
anantha krishnan's user avatar
3 votes
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Pre-processing: lemmatizing and stemming make a better doc2vec?

I have a project which I will turn documents of a corpus into doc2vec. I was reading that when people convert a document to bag of words they try to improve the bag of words by removing stopwords, ...
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how to improve doc2vec model

I would like to do some sentence embedding on around 500 sentences. The purpose is to find for new sentences, the most similar ones within the 500 sentences. Unfortunately, for now its definitely not ...
miki's user avatar
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Different accuracy scores for the vectors generated by Doc2Vec model trained on same hyper parameters

I am using doc2vec to generate vectors for sentences in training and testing datasets. The generated vectors are used to classify sentences using ensemble classifiers. The classifier is showing two ...
avinash's user avatar
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How should I formalize Doc2Vec Matrix Dimension?

Below, I have a simple diagram explaining the matrix dimension of word2vec. My goal is to expand this graph to incorporate document vectors for doc2vec. However, I'm having trouble understanding the ...
alpaca's user avatar
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How to train sentence/paragraph/document embeddings?

I'm well aware of word embeddings (word2vec or Glove) and I know of four papers treating the subject of more general embeddings : Distributed Representations of Sentences and Documents - Quoc V. Le,...
Pierre L.'s user avatar
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Why have a tanh layer, max pooling layer and then another tanh layer

I have been reading a Facebook paper, read here, and am confused about certain features of the architecture. I do not understand why they have a tanh layer, max-pooling layer, and then another tanh ...
Callum Kift's user avatar
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Clustering using doc2vec

is it okay to cluster documents by learned document vectors?. I think similar documents should have similar vectors. from this fact it is okay to cluster documents using for instance k-means. However, ...
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Can any one give explanation on LSA and what is different from NMF?

LSA is better way for extracted new concepts from large text documents collections .. in the following example : i have spend lot of time in Google to get explanation about the following My ...
Ray ben 's user avatar
5 votes
2 answers
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Doc2Vec for large documents

I have about 7000000 patents that I would like to do find the document similarity of. Obviously with a sample set that big it will take a long time to run. I am just taking a small sample of about ...
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