NLP301c quiz9

Porter stemmer is a process for removing the commoner _________and _________endings from words in English

• Issue & complex

• Words & eased

• Morphological & Inflexional

• None of the above

____ in this context is defined as the number of true positives divided by the total number of pairs that actually corefer.

• Recall

• Precision

• Anaphora

• None of the above

Which of the following equations is used to find the unigram probabilities using Add-1 smoothing?

• Count (wi)/N

• Count (wi)/(N+1)

• (Count (wi)+1)/(N+1)

• (Count (wi)+1)/(N+V)

Disadvantages of Porter Stemming algorithm are:

• The stems produced are not always real words.

• It has at least five steps and sixty rules and hence is time consuming

• It writes a string on one tape and a string on the other tape. Both strings have the same length.

• Ambiguity of a single word when it can be used as a verb, noun or an adjective.

What is Lexical Ambiguity?

• Ambiguity of a single word when it can be used as a verb, noun or an adjective.

• Words having many meanings.

• Sentences and words are not aligned.

• All of the above.

True or False: Word2Vec model is a machine learning model used to create vector notations of text objects. Word2vec contains multiple deep neural networks

• True

• False

Which of the following NLP problems can be solved with Hidden Markov Model (HMM)?

• POS tagging

• Speech recognition

• Spelling correction

• All of the above

Which of the following is not a problem when using Maximum Likelihood Estimation to obtain the parameters in a language model?

• Out-of-vocabulary items

• Over-fitting

• Smoothing

• Unreliable estimates when there is little training data

It is a process of generating a concise and meaningful __________ of ________ from multiple text resources such as books, news articles, blog posts, research papers, emails, and tweets.

• Definition, words

• Classification, symbols

• Summary, text

• Context, words

Which of the text parsing techniques can be used for noun phrase detection, verb phrase detection, subject detection, and object detection in NLP.

• Part of speech tagging

• Skip Gram and N-Gram extraction

• Continuous Bag of Words

• Dependency Parsing and Constituency Parsing