Questions tagged [word-embeddings]

Word embedding is the collective name for a set of language modeling and feature learning techniques in NLP where words are mapped to vectors of real numbers in a low dimensional space, relative to the vocabulary size.

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Vector representation of documents for text classification

I'm looking for proper method of document embeddings. I know that doc2vec will give me the vector representations for given corpus, but how do I embed new documents? I need to train neural network ...
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1 answer
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Some fundamental questions about Transformer

In the Transformer framework, a token as an input (time = $t$) $y^t$ is given by a sum of the original embedding of the token $x^t$ plus, a position embedding factor $v^t$, i.e., \begin{align} y^t = x^...
1 vote
1 answer
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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. ...
1 vote
1 answer
66 views

Which pre-trained model to select to generate embeddings from shop names written in English?

Good afternoon! I have a dataset with thousands of shop names written in English. Several shop names might belong to one business entity, for instance, shops with names "KFC 001", "WWW....
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1 answer
174 views

How to Calculate semantic similarity between video captions?

I intend to calculate the accuracy of a caption generated by comparing it to a number of reference sentences. For example, the captions for one video are as follows: These captions are for the same ...
0 votes
1 answer
96 views

A way to init sentence embedding for unsupervised text clustering, better than glove wordvec?

For unsupervised text clustering, the key thing is the init embedding for text. If we want to use deepcluster for text, the problem for text is how to get the init embedding from deep model. BERT can ...
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2 answers
714 views

Handling unknown words when making NER Models

I'm working on my custom Named Entity Recognition model that I'm making in Python's Keras lib. I have read that I should enumerate all words that are appearing, so that I get vectorized sequences. I ...
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2 answers
154 views

Is the number of bidirectional LSTMs in encoder-decoder model equal to the maximum length of input text/characters?

I'm confused about this aspect of RNNs while trying to learn how seq2seq encoder-decoder works at https://machinelearningmastery.com/configure-encoder-decoder-model-neural-machine-translation/. It ...
0 votes
1 answer
183 views

Question regarding training data in word2vec - skip-gram

I have a very simple question regarding the training data in word2vec. In the skip-gram implementation, the training data (if I understand it correctly) is generated as pairs of words like it's shown ...
2 votes
2 answers
212 views

Semantic Search

There is a problem we are trying to solve where we want to do semantic search on our set of data, i.e we have a domain specific data (example: sentences talking about automobiles) Our data is just a ...
1 vote
1 answer
817 views

Training Word2Vec with names instead of sentences

I have scientific database with articles and coauthors. using this database I am training word2vec model on co-authors. Use use case here is to disambiguate authors. I was wondering my approach here ...
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1 answer
258 views

how to train custom word2vec embeddings to find related articles?

I am beginner in machine learning. My project is to make search engine based on AI which shows related articles when we search on website. For this i decided to train my own embedding. I found two ...
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What is the state of art production search algorithm right now for semantic search? LSH? or other clustering method?

Trying to implement semantic search for high cardinality embedding for my own learning purpose, so far LSH seems promising, but I am wondering what is the state of algorithm big tech company are using ...
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Accuracy decreased after using google word2vec model for a sentiment classification [NLP][word-embedding]

I am using Amazon fine food reviews for a sentiment classification project. while I used my dataset corpus to train avg word2vec , I was getting an accuracy of 89 %. by using BOW and TF-IDF, i was ...
3 votes
1 answer
513 views

How to train millions of doc2vec embeddings using GPU?

I am trying to train a doc2vec based on user browsing history (urls tagged to user_id). I use chainer deep learning framework. There are more than 20 millions (user_id and urls) of embeddings to ...
1 vote
1 answer
79 views

Why the label is not explicitly involved in the loss function of skip-gram?

I am recently learning word embedding myself. When learning skip-gram from the paper https://arxiv.org/pdf/1310.4546.pdf[Distributed Representations of Words and Phrases and their Compositionality], I ...
3 votes
2 answers
198 views

How to train neural word embeddings?

So I am new to Deep Learning and NLP. I have read several blog posts on medium, towardsdatascience and papers where they talk about pre-training the word embeddings in an unsupervised fashion and then ...
0 votes
1 answer
136 views

Ensure trained word embeddings get high similarity with particular words

I am trying out my hand at training a Word2Vec model using gensim. I made a simple training file that basically had just one line repeated multiple times ...
7 votes
1 answer
9k views

How is WordPiece tokenization helpful to effectively deal with rare words problem in NLP?

I have seen that NLP models such as BERT utilize WordPiece for tokenization. In WordPiece, we split the tokens like playing to play and ##ing. It is mentioned that it covers a wider spectrum of Out-Of-...
2 votes
1 answer
379 views

Predicting word from a set of words

My task is to predict relevant words based on a short description of an idea. for example "SQL is a domain-specific language used in programming and designed for managing data held in a relational ...
3 votes
2 answers
1k views

Semantic search - combine text and image embedding

I have a question regarding combining text and image embeddings for semantic search. The use case is product search on a (B2B) marketplace, we have image(s) and title&description of the products. ...
0 votes
1 answer
673 views

Word-level text generation with word embeddings – outputting a word vector instead of a probability distribution

I am currently researching the topic of text generation for my university project. I decided (ofc) to go with a RNN getting a sequence of tokens as input with a target of predicting the next token ...
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1 answer
401 views

Word2vec CBOW model with negative sampling

From this article: In vanilla skip gram model, softmax is computationally very expensive, as it requires scanning through the entire output embedding matrix (W_output) to compute the probability ...
2 votes
1 answer
2k views

what is sentence embeding and how to do sentence embedding for a sentence and how to use word embedding to create a sentence embedding?

What is sentence embedding? How would you do sentence embedding for a sentence like: "How old are you?" How do you use word embedding to create a sentence ...
2 votes
1 answer
257 views

Improve results using user input

I've developed a tool that retrieve the closest expressions from a database based on what the user typed. (using word embedding - a comparison is made between each expression from the database and the ...
1 vote
1 answer
75 views

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 ...
2 votes
1 answer
186 views

The embeddings of GoogleNews-vectors-negative300.bin is stemmed or not?

I will use the embedding GoogleNews-vectors-negative300.bin data to do some project. Just want to know whether it was stemmed of the words?
3 votes
1 answer
215 views

How to build a symmetric similarity model on top of embeddings?

I have two equal length vectors that come out of two identical embedding layers. I want to calculate their similarity, and I don't trust the embedding layer enough to just use dot product (e.g. it's ...
0 votes
1 answer
90 views

Appropriate input size for nn.Embedding

I’m quite new to using Pytorch and deep learning. What size of unique categories of a categorical variable is appropriate for applying the nn.Embedding ideally (best practices)? for example, if a ...
1 vote
2 answers
1k views

How best to embed large and noisy documents

I have a large corpus of documents (web pages) collected from various sites of around 10k-30k chars each, I am processing them to extract relevant text as much as possible, but they are never perfect. ...
1 vote
2 answers
3k views

Dealing with multiple distinct-value categorical variables

So, I've got a dataset with almost all of its columns are categorical variables. Problem is that most of the categorical variables have so many distinct values. For instance, one column have more ...
0 votes
1 answer
778 views

How to compute sentence embedding from word2vec model?

I am new to NLP and I'm trying to perform embedding for a clustering problem. I have created the word2vec model using Python's gensim library, but I am wondering ...
0 votes
0 answers
19 views

Recommendation system NLP ideas

The problem: If we have a clustering problem with lets say x groups. And each group has a document describing it, lets say 3 pages. Then we have n observations each with a smaller piece of text ...
0 votes
1 answer
115 views

Why GloVe model (by gensim) does not have vectors for numbers 1, 2, ...?

I expected GLoVe to have vectors for numbers. from gensim import downloader as api glove = api.load("glove-twitter-25") glove['1'] This results in ...
1 vote
1 answer
248 views

How can I use Wikipedia2vec model for embedding my article named entities as 40% entities are not in a wikipedia?

I have news articles in my dataset containing named entities. I want to use the Wikipedia2vec model to encode the article's named entities. But some of the entities (around 40%) from our dataset ...
1 vote
3 answers
361 views

Does word2vec fail for window size equal to sentence size

Will word2vec fail if sentences contain only similar words, or in other words, if the window size is equal to the sentence size? I suppose this question boils down to whether word to vec considers ...
3 votes
1 answer
2k views

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 ...
0 votes
1 answer
1k views

Where can I find documentation or paper mentioning pre-trained distilbert-base-nli-mean-tokens model?

I am trying to find more information about pre-trained model distilbert-base-nli-mean-tokens. Can someone please point me to it's paper or documentation? Is it ...
8 votes
3 answers
4k views

Why is 10000 used as the denominator in Positional Encodings in the Transformer Model?

I was working through the All you need is Attention paper, and while the motivation of positional encodings makes sense and the other stackexchange answers filled me in on the motivations of the ...
3 votes
2 answers
708 views

Gensim doc2vec error: KeyError: "word 'senseless' not in vocabulary"

I am new to machine learning and tried doc2vec on quora duplicate dataset. new_dfx has columns 'question1' and 'question2' which has preprocessed questions in each row. Following is the tagged ...
2 votes
1 answer
2k views

Enable Mini-batch Processing on PyTorch Word Embeddings

I am new to PyTorch and trying to create word embeddings. I started with the example below and everything works fine and it completes relatively quickly. ...
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0 answers
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Best way to encode a tag column for clustering

I have a dataset which tells me a tech support case used a particular tech document. Every case has been tagged with which product it pertains to. Similarly tech documents are tagged with certain key ...
0 votes
1 answer
115 views

How to utilize dictionary data set for text classification?

I have a dataset similar to newsgroup20 for classification. With the training dataset, I have a dictionary data set that explains some jargons in the training dataset. These both are different data ...
2 votes
1 answer
472 views

Range of values of BERT and other embeddings?

Are the values in all NLP models' embeddings between the range -1 to 1? If not, what models use a different range (or decimal points)? And what could be the reason for that shift/change?
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0 answers
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Predict best chess move using RNNs

I am trying to do a project with AI: in which during any certain moment of a chess game i can predict, using a RNN trained on a kaggle dataset, the best possible move that i can make. I am having ...
44 votes
5 answers
42k views

Intuitive explanation of Noise Contrastive Estimation (NCE) loss?

I read about NCE (a form of candidate sampling) from these two sources: Tensorflow writeup Original Paper Can someone help me with the following: A simple explanation of how NCE works (I found the ...
1 vote
2 answers
4k views

How to calculate the mean average of word embedding and then compare strings using sklearn.metrics.pairwise

I am totally new to this topic, that's why I am so confused or stuck in this code for a while, but I am not sure how to solve it correctly. My goal is to write a short text embedding using vector ...
0 votes
1 answer
331 views

Train Word Embeddings on new vocabulary given the pre trained embeddings through word2vec

I have the pre-trained Embbedings on the language. I have the vocabulary for that language, what would be the pipeline to train this vocabulary by using Pre train embeddings through the word2vec model?...
1 vote
1 answer
286 views

BERT is running out of memory in forward pass for my dictionary

Running code from this answer, my BERT is running out for my 4k words dictionary. I don't need to do anything with these words yet, just make embeddings for my data. So, using this exactly: ...
0 votes
1 answer
197 views

Getting context-word pairs for a continuous bag of words model and other confusions

Suppose I have a corpus with documents: ...

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