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]
Question
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]
Solution
The question is asking for the use of the Gensim library in Python to solve word analogy problems and to find different forms of words using word embeddings. Here's how you can do it:
First, you need to import the necessary libraries and load the word2vec model:
from nltk.data import find
import gensim
word2vec_sample = str(find('models/word2vec_sample/pruned.word2vec.txt'))
model = gensim.models.KeyedVectors.load_word2vec_format(word2vec_sample, binary=False)
i. Solving the analogy problems:
A. Man is to priest as woman is to ____
model.most_similar(positive=['woman', 'priest'], negative=['man'], topn=3)
B. They is to their as we is to ___
model.most_similar(positive=['we', 'their'], negative=['they'], topn=3)
C. Russia is to Moscow as Spain is to ___
model.most_similar(positive=['Spain', 'Moscow'], negative=['Russia'], topn=3)
D. Long is to longest as old is to ___
model.most_similar(positive=['old', 'longest'], negative=['long'], topn=3)
ii. Finding different forms of words:
A. Past tenses of verbs:
model.most_similar(positive=['came', 'have'], negative=['come'], topn=3)
model.most_similar(positive=['had', 'buy'], negative=['have'], topn=3)
model.most_similar(positive=['bought', 'come'], negative=['buy'], topn=3)
B. Singular forms of verbs:
model.most_similar(positive=['comes', 'have'], negative=['come'], topn=3)
model.most_similar(positive=['has', 'be'], negative=['have'], topn=3)
model.most_similar(positive=['is', 'come'], negative=['be'], topn=3)
C. Plural forms of nouns:
model.most_similar(positive=['cards', 'child'], negative=['card'], topn=3)
model.most_similar(positive=['children', 'card'], negative=['child'], topn=3)
Please note that the results will depend on the specific word2vec model you are using. The model might not always return the expected results due to the limitations of the word embeddings.
Similar Questions
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]
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
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
13.Which NLP technique is used for finding similar words or documents based on their semantic meaning? A. Lemmatization B. Word Embeddings C. Sentiment Analysis D. Information Extraction
What do N-gram models represent in natural language processing? Question 6Answera.A model that captures the context of words in a sentenceb.A model that represents text as a set of unique words with their respective counts, considering sequences of n wordsc.A model that identifies and classifies named entities in textd.A model that predicts the next word in a sequence of text
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