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Questions tagged [bag-of-words]

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Unsupervised learning with bags of words with a word metric

I would like to perform clustering on a collection of documents with the assumption that I have a metric $\rho$ which tells me how close two words are to being synonyms. If $\mathcal{W}$ is our ...
jwhite's user avatar
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1 answer
109 views

Which should we choose: sequence-model vs n-gram model and why does it depend on ratio of Samples / Words per Sample

This ML tutorial from Google is analyzing the imdb reviews dataset to predict the tag positive or negative. When choosing a model Calculate the number of samples/number of words per sample ratio. If ...
Nate Anderson's user avatar
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1 answer
578 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 ...
Mahesha999's user avatar
1 vote
1 answer
629 views

Is n-gram a special instance of bag of word? What are their differences?

Is n-gram a special instance of bag of word? What are their differences? From my understanding, n-gram is when replacing the words in bag of words with n-grams, and follow the same procedures to ...
Student's user avatar
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1 answer
234 views

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

Suppose I have a corpus with documents: ...
sangstar's user avatar
  • 133
2 votes
1 answer
811 views

Should bag of words in training set include test set data when doing text classification?

I'm doing text classification to identify 'attacks' from Wikipedia comments using a simple bag of words model and a linear SVM classifier. Because of class imbalance, I'm using the F1 score as my ...
jonnyf's user avatar
  • 121
2 votes
1 answer
49 views

Usage of KL divergence to improve BOW model

For a university project, I chose to do sentiment analysis on a Google Play store reviews dataset. I obtained decent results classifying the data using the bag of words (BOW) model and an ADALINE ...
Balocre's user avatar
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1 vote
2 answers
1k views

How to decide to go with BOW or TFIDF

I know that there are methods that help in selecting features such as Matual Info, and Info Gain, etc. But for datasets with thousands of records and thousands of features it is time consuming to ...
asmgx's user avatar
  • 549
1 vote
2 answers
497 views

How to decide which method to use TFIDF, or BOW

In a huge dataset for NLP it is taking very long time to classify my dataset therefore, trying each feature extraction method separetly is time consuming and not effecient. Is there a way that can ...
asmgx's user avatar
  • 549
1 vote
1 answer
24 views

Which phrase should be returned in case of multiple matches when comparing text?

I want to compare one sentence to some other sentences using the Bag of Words model. Suppose that my comparing sentence is: I am playing football and there are three more sentences that I want to ...
Hefaz's user avatar
  • 113
1 vote
2 answers
192 views

Bag-of-words and Spam classifiers

I implemented a spam classifier using Bernoulli Naive Bayes, Logistic Regression, and SVM. Algorithms are trained on the entire Enron spam emails dataset using the Bag-of-words (BoW) approach. ...
user529295's user avatar
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How to use scikit-learn to extract features from text when I only have positive and unlabeled data?

I'm looking for something similar to this https://scikit-learn.org/stable/auto_examples/text/plot_document_classification_20newsgroups.html#sphx-glr-auto-examples-text-plot-document-classification-...
rbaehr's user avatar
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1 answer
1k views

Why I would use TF-IDF after Bag-of-Words (CountVectorizer)?

In my recent studies over Machine Learning NLP tasks I found this very nice tutorial teaching how to build your first text classifier: https://towardsdatascience.com/machine-learning-nlp-text-...
Tiago Bachiega de Almeida's user avatar
1 vote
1 answer
190 views

One-hot vector for fixed vocabulary

given a vocabulary with $|V|=4$ and V = {I, want, this, cat} for example. How does the bag-of-words representation with this vocabulary and one-hot encoding look like regarding example sentences: You ...
Mi.'s user avatar
  • 113
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1 answer
312 views

Machine learning algorithms for forming Homophones from input dataset word

https://www.google.com/search?sxsrf=ALeKk01_SgA8G4UfNm4rOqku4yJBFvKhLw%3A1600154854621&source=hp&ei=5mxgX8ztI6KZ4-EPq-mL8Ak&q=homophones+example&oq=Homophones&gs_lcp=...
Prashant Akerkar's user avatar
1 vote
1 answer
146 views

Machine learning algorithms for correct words formation from jumbled words

https://www.google.com/search?q=jumbled+words&oq=jumbled&aqs=chrome.1.69i57j0l4.3399j0j9&client=ms-android-lava&sourceid=chrome-mobile&ie=UTF-8 Can Machine learning algorithms ...
Prashant Akerkar's user avatar
0 votes
2 answers
2k views

Is it good practice to remove the numeric values from the text data during preprocessing?

Im doing preprocessing on a text dataset. I have certain numerics in it like: date(1st July) year(2019) tentative values (3-5 years/ 10+ advantages). unique values (room no 31/ user rank 45) ...
emily 's user avatar
  • 35
1 vote
3 answers
2k views

How to process the hyphenated english words for any nlp problem?

Im doing preprocessing on english text dataset. I encounter hyphenated words like 'well-known'. Will it be useful if I remove the hyphen as special character and treat it as a single word 'wellknown' ...
emily 's user avatar
  • 35
2 votes
1 answer
173 views

Word representation that gives more weight to terms frequent in corpus?

The tf-idf discounts the words that appear in a lot of documents in the corpus. I am constructing an anomaly detection text classification algorithm that is trained only on valid documents. Later I ...
Borut Flis's user avatar
4 votes
0 answers
192 views

Bag of words: Prediction on new (out-of-sample) data

I'm working with a bag of words in R: library(tm) corpus = VCorpus(textsource) dtm = DocumentTermMatrix(corpus) dtm = as.matrix(dtm) I use the matrix ...
Peter's user avatar
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