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Bag-of-words

What is the bag-of-words model?

The bag-of-words model is a method used to represent text data when we model text using machine learning algorithms. It is the simplest form of text representation in numbers. It is extremely easy, both to understand and to implement, and is used for language modeling and document classification.

It is a way to extract features from text to be used in modeling. 

A bag-of-words includes a vocabulary of known words and a measure of the presence of known words. It describes the occurrence of words in a document.

The model only bothers only about whether known words show up in the document. It does not care where they show up in the document, only that they do show up.

It tries to learn about the meaning of a document from its content alone and assumes that if documents have similar content, they are similar to each other.

We cannot directly feed text into algorithms applied in NLP. They work on numbers. The model converts the text into a bag-of-words. The bag-of-words keeps a count of the occurrences of the most frequently occurring words in that text.

The model counts the number of times each word appears and turns text into fixed-length vectors.


How does the bag-of-words model work? <Or> How to apply the bag-of-words model?

Here are the steps involved in applying the bag-of-words model:

The first step is to pre-process the data. The text needs to be converted into lower case, all non-word characters need to be removed, and all punctuations need to be removed.

In the next step, we have to find the most frequent words in the text. The vocabulary must be defined, each sentence must be tokenized to words, and then the number of times the word occurs must be counted.

After that, the model is constructed. A vector is built to determine whether a word is a frequent word. If it is a frequent word, it is set as 1 and if not, it is set as 0.

And now you get your output.


What are the limitations and drawbacks of the bag-of-words model?

The bag-of-words model is rather easy to understand and to implement, but it does have some limitations and drawbacks. 

The vocabulary/dictionary needs to be designed very carefully. Its size has an impact on the sparsity of the document representations and must be managed well.

The model ignores context by discarding the meaning of the words and focusing on frequency of occurrence. This can be a major problem, because the arrangement of the words in a sentence can completely change the meaning of the sentence and the model cannot account for this.

Another major drawback of this model is that it is rather difficult to model sparse representations. This is due to informational reasons as well as computational reasons. The model finds it difficult to harness a small amount of information in a vast representational space.


The biggest advantage of the bag-of-words model

The most significant advantage of the bag-of-words model is its simplicity and ease of use. It can be used to create an initial draft model before proceeding to more sophisticated word embeddings.

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Bag-of-words

October 14, 2020

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What is the bag-of-words model?

The bag-of-words model is a method used to represent text data when we model text using machine learning algorithms. It is the simplest form of text representation in numbers. It is extremely easy, both to understand and to implement, and is used for language modeling and document classification.

It is a way to extract features from text to be used in modeling. 

A bag-of-words includes a vocabulary of known words and a measure of the presence of known words. It describes the occurrence of words in a document.

The model only bothers only about whether known words show up in the document. It does not care where they show up in the document, only that they do show up.

It tries to learn about the meaning of a document from its content alone and assumes that if documents have similar content, they are similar to each other.

We cannot directly feed text into algorithms applied in NLP. They work on numbers. The model converts the text into a bag-of-words. The bag-of-words keeps a count of the occurrences of the most frequently occurring words in that text.

The model counts the number of times each word appears and turns text into fixed-length vectors.


How does the bag-of-words model work? <Or> How to apply the bag-of-words model?

Here are the steps involved in applying the bag-of-words model:

The first step is to pre-process the data. The text needs to be converted into lower case, all non-word characters need to be removed, and all punctuations need to be removed.

In the next step, we have to find the most frequent words in the text. The vocabulary must be defined, each sentence must be tokenized to words, and then the number of times the word occurs must be counted.

After that, the model is constructed. A vector is built to determine whether a word is a frequent word. If it is a frequent word, it is set as 1 and if not, it is set as 0.

And now you get your output.


What are the limitations and drawbacks of the bag-of-words model?

The bag-of-words model is rather easy to understand and to implement, but it does have some limitations and drawbacks. 

The vocabulary/dictionary needs to be designed very carefully. Its size has an impact on the sparsity of the document representations and must be managed well.

The model ignores context by discarding the meaning of the words and focusing on frequency of occurrence. This can be a major problem, because the arrangement of the words in a sentence can completely change the meaning of the sentence and the model cannot account for this.

Another major drawback of this model is that it is rather difficult to model sparse representations. This is due to informational reasons as well as computational reasons. The model finds it difficult to harness a small amount of information in a vast representational space.


The biggest advantage of the bag-of-words model

The most significant advantage of the bag-of-words model is its simplicity and ease of use. It can be used to create an initial draft model before proceeding to more sophisticated word embeddings.

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