Questions tagged [bag-of-words]
For questions about the bag-of-words model, which is often used in natural language processing and information retrieval.
9 questions
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Validating bundles of XMLs with inconsistent structure
The problem:
We have a large number of XML bundles, and each bundle needs to meet certain criteria to be considered valid; specifically, certain values (or types of values) should belong to certain ...
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Why does alphago only go to 19 numbers and 19 alphabet letter and not 26 numbers and 26 alphabet letters? Alphago max version?
I was just looking at alpha go videos on youtube that google deep mind sent me and was wondering on the versioning board and it reminded me of a more advanced version of chess game on windows ...
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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 ...
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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 ...
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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. ...
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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 ...
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What is the meaning of "continuous" in a continuous bag-of-words model?
The word continuous in mathematics is a property of either a set or a function that says that the underlying object has no discontinuity in the range mentioned. If the object is a set, then $[-1,1]$ ...
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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 ...
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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 ...
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