Q.5 In NLP, The algorithm decreases the weight for commonly used words and increases the weight for words that are not used very much in a collection of documents1. Inverse Document Frequency (IDF)2. Term Frequency (TF)3. Word2Vec4. Latent Dirichlet Allocation (LDA)

The answer is 1. Inverse Document Frequency (IDF).

Here's a step-by-step explanation:

1. In Natural Language Processing (NLP), there are several methods to represent words and documents, one of which is the TF-IDF (Term Frequency-Inverse Document Frequency) method.

2. The TF-IDF method consists of two parts: Term Frequency (TF) and Inverse Document Frequency (IDF).

3. Term Frequency (TF) is a measure of how frequently a term occurs in a document. If a word occurs frequently in a document, its TF value increases.

4. On the other hand, Inverse Document Frequency (IDF) is a measure of how important a term is. It decreases the weight for commonly used words and increases the weight for words that are not used very much in a collection of documents.

5. Therefore, the algorithm that decreases the weight for commonly used words and increases the weight for words that are not used very much in a collection of documents is the Inverse Document Frequency (IDF).

6. Word2Vec and Latent Dirichlet Allocation (LDA) are other methods used in NLP, but they do not specifically decrease the weight for commonly used words and increase the weight for less used words.

The answer is 1. Inverse Document Frequency (IDF).

Here's a step-by-step explanation:

1. In Natural Language Processing (NLP), there are several algorithms used to process and analyze large amounts of natural language data.

2. Term Frequency (TF) is a measure of how frequently a term occurs in a document. It doesn't consider the importance of the word in the document.

3. Word2Vec is a technique for natural language processing. The word2vec algorithm uses a neural network model to learn word associations from a large corpus of text.

4. Latent Dirichlet Allocation (LDA) is a generative statistical model that allows sets of observations to be explained by unobserved groups that explain why some parts of the data are similar.

5. Inverse Document Frequency (IDF) is a measure of how much information the word provides, i.e., if it's common or rare across all documents. It is the logarithmically scaled inverse fraction of the documents that contain the word, obtained by dividing the total number of documents by the number of documents containing the term, and then taking the logarithm of that quotient.

6. Therefore, the algorithm that decreases the weight for commonly used words and increases the weight for words that are not used very much in a collection of documents is the Inverse Document Frequency (IDF).