The examples on the left-hand side match with the technique, model or method on the right-hand side as follows:

1. ['Could', 'have', 'been', 'much', 'better'] - This is an example of tokenisation. In Natural Language Processing (NLP), tokenisation is the process of breaking down text into words, phrases, symbols, or other meaningful elements called tokens.

2. [('when','i'),('has','been'),('was','the'),('the','matter'),('if','you')] - This is an example of the N-gram model. In NLP, an N-gram is a contiguous sequence of n items from a given sample of text or speech. In this case, it's a 2-gram (or bigram) model because each sequence contains 2 words.

3. He is singing: [[1,0,0,0,0,0,0,0,0],[0,0,1,0,0,0,0,0,0],[0,0,0,1,0,0,0,0,0]] - This is an example of one-hot representation. In NLP, one-hot representation is a method of representing words where each word in the vocabulary is represented as a vector in n-dimensional space where n is the size of the vocabulary. Each word is represented as a vector with a 1 in its position in the vocabulary and 0s in all other positions.

4. ([-0.335107, -0.009982, 0.680831, -0.023788, -0.165798,•••,0.345986, -0.232295, 0.021137,0.08515 , -0.24387]) - This is an example of a word vector. In NLP, a word vector is a numerical representation of a word that communicates its relationship to other words. Each word is mapped to a vector of real numbers representing its distributional semantics, learned from large amounts of text data.

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The examples on the left-hand side match with the technique, model or method on the right-hand side as follows:

1. ['Could', 'have', 'been', 'much', 'better'] - This is an example of tokenisation. In Natural Language Processing (NLP), tokenisation is the process of breaking down text into words, phrases, symbols, or other meaningful elements called tokens.

2. [('when','i'),('has','been'),('was','the'),('the','matter'),('if','you')] - This is an example of the N-gram model. In NLP, an N-gram is a contiguous sequence of n items from a given sample of text or speech. In this case, it's a 2-gram (or bigram) model because each sequence contains 2 words.

3. He is singing: [[1,0,0,0,0,0,0,0,0],[0,0,1,0,0,0,0,0,0],[0,0,0,1,0,0,0,0,0]] - This is an example of one-hot representation. In NLP, one-hot representation is a method of representing words where each word in the vocabulary is represented as a vector in n-dimensional space where n is the size of the vocabulary. Each word is represented as a vector with a 1 in its position in the vocabulary and 0s in all other positions.

4. ([-0.335107, -0.009982, 0.680831, -0.023788, -0.165798,•••,0.345986, -0.232295, 0.021137,0.08515 , -0.24387]) - This is an example of a word vector. In NLP, a word vector is a numerical representation of a word that communicates its relationship to other words. Each word is mapped to a vector of real numbers representing its distributional semantics, learned from large amounts of text data.

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The examples on the left-hand side match with the technique, model or method on the right-hand side as follows:

1. ['Could', 'have', 'been', 'much', 'better'] - This is an example of tokenisation. In Natural Language Processing (NLP), tokenisation is the process of breaking down text into words, phrases, symbols, or other meaningful elements called tokens.

2. [('when','i'),('has','been'),('was','the'),('the','matter'),('if','you')] - This is an example of the N-gram model. In NLP, an N-gram is a contiguous sequence of n items from a given sample of text or speech. In this case, it's a 2-gram (or bigram) model because each sequence contains 2 words.

3. He is singing: [[1,0,0,0,0,0,0,0,0],[0,0,1,0,0,0,0,0,0],[0,0,0,1,0,0,0,0,0]] - This is an example of one-hot representation. In NLP, one-hot representation is a method of representing words where each word in the vocabulary is represented as a vector in n-dimensional space where n is the size of the vocabulary. Each word is represented as a vector with a 1 in its position in the vocabulary and 0s in all other positions.

4. ([-0.335107, -0.009982, 0.680831, -0.023788, -0.165798,•••,0.345986, -0.232295, 0.021137,0.08515 , -0.24387]) - This is an example of a word vector. In NLP, a word vector is a numerical representation of a word that communicates its relationship to other words. Each word is mapped to a vector of real numbers representing its distributional semantics, learned from large amounts of text data.

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