What should the Specialist do to meet these requirements?
Create one-hot word encoding vectors.
Produce a set of synonyms for every word using Amazon Mechanical Turk.
Create word embedding vectors that store edit distance with every other word.
Download word embeddings pre-trained on a large corpus.
Explanations:
One-hot encoding represents words as vectors where each word corresponds to a unique index. This results in high-dimensional, sparse representations that do not capture semantic similarity or context effectively, making it unsuitable for finding similar words.
Generating synonyms using Amazon Mechanical Turk involves human annotation, which can be inconsistent and time-consuming. This approach does not leverage the vast contextual relationships found in large text corpora and is not suitable for a scalable or automated nearest neighbor model.
Creating word embedding vectors based on edit distance would focus on spelling differences rather than contextual or semantic similarities. Edit distance does not account for the meanings of words, thus failing to meet the needs of the nearest neighbor model for contextually similar words.
Downloading pre-trained word embeddings, such as Word2Vec or GloVe, provides dense vector representations that capture semantic relationships and contextual similarities learned from large corpora. This approach is effective for the nearest neighbor model as it directly addresses the requirement for words used in similar contexts.
I outline that the answer is:
Download word embeddings pre-trained on a large corpus.
From my point of view, the answer is:
Download word embeddings pre-trained on a large corpus.