The statement "One-hot vector representation of words in a sequence captures long-term dependencies" is **False**.

### Explanation:
One-hot vectors are a basic form of vector representation where each word in a vocabulary is represented as a binary vector. In this representation, the vector has a length equal to the total number of words in the vocabulary, and one element is set to 1 (indicating the presence of a specific word), while all other elements are set to 0.

However, one-hot encoding does not capture the relationships or dependencies between words, especially in the context of sequences or sentences. Each word is treated independently, with no information about their order or context rooted in the sequence, which is critical for understanding long-term dependencies in linguistic data.

More advanced models, such as Word2Vec or Recurrent Neural Networks (RNNs), are needed to capture such dependencies effectively.

Question

The statement "One-hot vector representation of words in a sequence captures long-term dependencies" is **False**.

### Explanation:
One-hot vectors are a basic form of vector representation where each word in a vocabulary is represented as a binary vector. In this representation, the vector has a length equal to the total number of words in the vocabulary, and one element is set to 1 (indicating the presence of a specific word), while all other elements are set to 0.

However, one-hot encoding does not capture the relationships or dependencies between words, especially in the context of sequences or sentences. Each word is treated independently, with no information about their order or context rooted in the sequence, which is critical for understanding long-term dependencies in linguistic data.

More advanced models, such as Word2Vec or Recurrent Neural Networks (RNNs), are needed to capture such dependencies effectively.

Knowee AI · Accepted Answer

The statement "One-hot vector representation of words in a sequence captures long-term dependencies" is **False**.

### Explanation:
One-hot vectors are a basic form of vector representation where each word in a vocabulary is represented as a binary vector. In this representation, the vector has a length equal to the total number of words in the vocabulary, and one element is set to 1 (indicating the presence of a specific word), while all other elements are set to 0.

However, one-hot encoding does not capture the relationships or dependencies between words, especially in the context of sequences or sentences. Each word is treated independently, with no information about their order or context rooted in the sequence, which is critical for understanding long-term dependencies in linguistic data.

More advanced models, such as Word2Vec or Recurrent Neural Networks (RNNs), are needed to capture such dependencies effectively.

One-hot vector representation of words in a sequence captures long-term dependencies.1 pointTrueFalse

Question

One-hot vector representation of words in a sequence captures long-term dependencies.

Solution

Explanation:

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