Problem 4. Word embedding is a technique in language processing, where words are mapped to vectors of numbers such that words with similar meaning map to similar vectors. F Download and unpack precomputed word embeddings from http :/1p . st anf ord . edu/ data: glove . 6B . zip (822 MB). Each file contains 400,000 words in the decreasing order of frequency along with the associated vectors. Here is an example code to load 100dimensional vectors for 10,000 most common words and print the vector for the word "ocean": def load_embeddings(filename, num_words): embeddings = {} with open(filename) as file: for line in itertools.islice(file, num_words): tokens = line split() word, vector = tokensEG], tokensElz] embeddingszord] = [f10at(x) for x in vector] return embeddings embeddings = load_embeddings(\"glove.6B.100d.txt\