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Is there a metric to compare BOW vs TFIDF results?

I am working on a document search task and have used Bag of Words (BOW) and TFIDF vectorization techniques. My observation after going through some sample searches are - Both of them seem to provide ...
Amit Pathak's user avatar
0 votes
2 answers
78 views

How to represent multi-label colours in one-hot encoding?

Say I want to predict the price of a gemstone based on its colour. I have two options: averaging over its colour on an RGB scale, or using its textual description. If I was to choose the latter, how ...
GeorgeWTrump's user avatar
0 votes
1 answer
650 views

Which data representation of text as input for NLP Deep Learning models?

I have been given a data set with 30.000 text documents (each text file is rather small with respect to its length and consists in most cases of around 20 sentences), which are labelled with 0 or 1. ...
MiFischer22's user avatar
1 vote
1 answer
95 views

Bag of Tricks: n-grams as additional features?

I've been playing with PyTorch's nn.EmbeddingBag for sentence classification for about a month. I've been doing some feature engineering, playing with different ...
rocksNwaves's user avatar
2 votes
1 answer
180 views

How many parameter would there be in a logistic regression model used to classify reviews into "good" or "bad"?

Suppose we want to classify a review as good ($1$) or bad ($0$). We have a training data set of $10,000$ reviews. Also, suppose we have a vocabulary of $100,000$ words $w_1, \dots, w_{100,000}$. So ...
aiguy123's user avatar
8 votes
1 answer
197 views

Why are documents kept separated when training a text classifier?

Most of the literature considers text classification as the classification of documents. When using the bag-of-words and Bayesian classification, they usually use the statistic TF-IDF, where TF ...
freesoul's user avatar
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