What should the Specialist do to meet these requirements?

An interactive online dictionary wants to add a widget that displays words used in similar contexts. A Machine Learning Specialist is asked to provide word features for the downstream nearest neighbor model powering the widget.

What should the Specialist do to meet these requirements?
A . Create one-hot word encoding vectors.
B . Produce a set of synonyms for every word using Amazon Mechanical Turk.
C . Create word embedding factors that store edit distance with every other word.
D . Download word embedding’s pre-trained on a large corpus.

View Answer

Answer: D

Explanation:

Word embeddings are a type of dense representation of words, which encode semantic meaning in a vector form. These embeddings are typically pre-trained on a large corpus of text data, such as a large set of books, news articles, or web pages, and capture the context in which words are used. Word embeddings can be used as features for a nearest neighbor model, which can be used to find words used in similar contexts. Downloading pre-trained word embeddings is a good way to get started quickly and leverage the strengths of these representations, which have been optimized on a large amount of data. This is likely to result in more accurate and reliable features than other options like one-hot encoding, edit distance, or using Amazon Mechanical Turk to produce synonyms.

Reference: https://aws.amazon.com/blogs/machine-learning/amazon-sagemaker-object2vec-adds-new-features-that-support-automatic-negative-sampling-and-speed-up-training/