Explain in general terms how word embeddings can be said to represent the meaningsof words, and relations such as similarity and analogy between words. Your answershould include brief definitions of the following terms, with appropriate examples:• Syntagmatic association or first-order co-occurrence.• Paradigmatic association or second-order co-occurrence.• The parallelogram model of relational similarity

Software:You will need both NLTK and the gensim packages installed on your computer. It should bestraightforward to install gensim using pip or conda.a) Explain in general terms how word embeddings can be said to represent the meaningsof words, and relations such as similarity and analogy between words. Your answershould include brief definitions of the following terms, with appropriate examples:• Syntagmatic association or first-order co-occurrence.• Paradigmatic association or second-order co-occurrence.• The parallelogram model of relational similarity.[20 marks]b) It turns out that the way word embeddings model similarity and analogy can capture avariety of semantic relations between words. Follow the methods used in the Bird tutorialfor the queries below, using the NLTK excerpt from the Google News model:>>> from nltk.data import find>>> word2vec_sample = str(find('models/word2vec_sample/pruned.word2vec.txt'))>>> model = gensim.models.KeyedVectors.load_word2vec_format(word2vec_sample,binary=False)In each case, you should specify the top three words that match the query, and discusswhich of them (if any) come closest to your expected answer.i. Show how gensim solves the following queries:A. Man is to priest as woman is to ____B. They is to their as we is to ___C. Russia is to Moscow as Spain is to ___D. Long is to longest as old is to ___ii. It turns out that embeddings can capture morphosyntactic features such asnumber, tense, and case. Write gensim queries that will return:A. Past tenses of verbs, e.g. come -> came, have -> had, buy -> bought.B. Singular forms of verbs, e.g. come -> comes, have -> has, be -> is.C. Plural forms of nouns, e.g. card -> cards, child -> children.[15 marks]

What does it mean to say words carry associations?A.A word's origins and use in other contexts affect the way we read it.B.Words that came from foreign languages are more interesting than others.C.Two words that are spelled the same may be pronounced differently.D.Words are best understood through synonyms and antonyms.SUBMITarrow_backPREVIOUS

It turns out that the way word embeddings model similarity and analogy can capture avariety of semantic relations between words. Follow the methods used in the Bird tutorialfor the queries below, using the NLTK excerpt from the Google News model:>>> from nltk.data import find>>> word2vec_sample = str(find('models/word2vec_sample/pruned.word2vec.txt'))>>> model = gensim.models.KeyedVectors.load_word2vec_format(word2vec_sample,binary=False)In each case, you should specify the top three words that match the query, and discusswhich of them (if any) come closest to your expected answer.i. Show how gensim solves the following queries:A. Man is to priest as woman is to ____B. They is to their as we is to ___C. Russia is to Moscow as Spain is to ___D. Long is to longest as old is to ___ii. It turns out that embeddings can capture morphosyntactic features such asnumber, tense, and case. Write gensim queries that will return:A. Past tenses of verbs, e.g. come -> came, have -> had, buy -> bought.B. Singular forms of verbs, e.g. come -> comes, have -> has, be -> is.C. Plural forms of nouns, e.g. card -> cards, child -> children.[15 marks]

What is a key advantage of word vector embeddings compared to the Bag-of-Words model?AReduced computational complexityBSimplicity and ease of implementationCBetter handling of out-of-vocabulary wordsDAbility to capture semantic relationships between words

Word vectorization captures which kind of linguistic relationships?Question 11Answera.Semanticb.Syntactic