NLP301c quiz6

A transducer maps between FST one set of _________and another.

• Words

• Letters

• Symbols

• Sentences

Benefits of sentiment analysis includes:

• Sentiment analysis helps businesses process huge amounts of data in an efficient and cost-effective way.

• Sentiment analysis models can help you immediately identify these kinds of situations and gauge brand sentiment, so you can take action right away.

• Tagging text by sentiment is highly subjective, influenced by personal experiences, thoughts, and beliefs. By using a centralized sentiment analysis system, companies can apply the samecriteria to all of their data, helping them improve accuracy and gain better insights.

• All of the above

the sense of the word depends on the words of that particular word.

• Relating

• Same

• Neighboring

• Dependent

Given a stream of text, Named Entity Recognition determines which pronoun maps to which noun.

• True

• False

_________ is a vocabulary, a list of words, a dictionary.

• Corpus

• WordNet

• Lexicon

• None of the above

You have created a document term matrix of the data, treating every tweet as one document. Which of the following is correct, in regards to document term matrix?

• Removal of stopwords from the data will affect the dimensionality of data

• Normalization of words in the data will reduce the dimensionality of data

• Converting all the words in lowercase will not affect the dimensionality of the data

Machine Learning & Statistical Inference are the popular methods for implementing___.

• Lexical Analysis

• Tokens and Quantifiers

• NLP

• None of the above

Natural Language generation is the main task of Natural language processing.

• True

• False

Assume a corpus with 350 tokens in it. We have 20 word types in that corpus (V = 20). The frequency (unigram count) of word types "short" and "fork" are 25 and 15 respectively. Which of the following is the probability of "short" (PMLE("short"))?

• 25/350

• 26/370

• 26/350

• 25/370

Social Media platforms are the most intuitive form of text data. You are given a corpus of complete social media data of tweets. How can you create a model that suggests the hashtags?

• Perform Topic Models to obtain most significant words of the corpus

• Train a Bag of Ngrams model to capture top n-grams - words and their combinations

• Train a word2vector model to learn repeating contexts in the sentences

• All of these