Bag of words (BoW) is a statistical language model that uses word count to assess text and documents. The model does not take into account the order of words inside a document. BoW can be implemented as a Python dictionary, with each key corresponding to a word and each value corresponding to the number of times that word appears in a text.
NLP
Feature Extraction
In NLP, feature extraction (or
vectorization) is the process of converting text into a BoW vector, with
features being unique words and feature values being word counts.
Bag-of-words Test Results
Bag-of-words test data is new text that has
been transformed to a BoW vector with the use of a trained features dictionary.
Based on the index mapping of the learned features dictionary, the new test
data may be translated to a BoW vector.
The
Feature Vector
A feature vector is a numerical
representation of an object's prominent properties in machine learning. The
objects in bag-of-words (BoW) are text samples, Sentiment Analysis and the characteristics are word counts.
NLP
Language Smoothing
Language smoothing is a technique used in
NLP to minimize overfitting. It takes a little amount of probability from known
words and distributes it to unknown words. As a result, the unknown words have
a probability greater than zero.
NLP
includes a dictionary.
A features dictionary is a mapping from
each distinct word in the training data to a distinct index. This is used to
create vectors from a bag of words.
For example, given the training data
"Squealing suitcase squids are not like typical squids," the features
dictionary would look like this.
Bag-of-words
Scarcity of data
Bag-of-words has less data sparsity than
other statistical models (i.e., more training information to draw from). When
vectorizing a text, the vector is termed sparse if the majority of its values
are 0, indicating that the majority of the words are not in the vocabulary
design. BoW is also less prone to overfitting (adapting a model too strongly to
training data). If you are looking for the best Keyword density checker, contact us at WebTool!
NLP
Perplexity
The optimum language model for text
prediction tasks is one that can anticipate an unknown test text (gives the
highest probability). The model is said to have lesser confusion in this
situation.
Bag of words has a larger confusion (it predicts real
language less well) than other models. For example, if you use a Markov chain
for text prediction using a bag-of-words, you can receive a nonsensical
sequence like "we I there your your."
Meanwhile, a trigram model may provide the
significantly more comprehensible (albeit still strange): I assign to his
dreams to adore beauty."
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