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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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Improve the accuracy of semantic text matching

I have a corpus of ~200K sentences of variable length, the median length is 16 words. My goal is for a given sentence to find other sentences with a similar meaning. I tried several approaches: using ...
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How PV-DBOW works

The authors of the Paragraph Vector paper describe PV-DBOW with: 2.3. Paragraph Vector without word ordering: Distributed bag of words The above method considers the concatenation of the ...
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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 ...
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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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Paragraph vector model robustness

I'm using paragraph vector (doc2vec) to model phrases. Though I am getting good results, and it is even capable of finding similarity between two phrases even when there are no common words, there is ...
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714 views

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 ...
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148 views

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 ...
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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 ...
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1k views

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,...
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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 ...
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941 views

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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486 views

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 ...
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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 ...