Natural Language Processing

Question 1

What is the Goal of Natural Language Processing?

Answer 1

To make machines understand and interpret human language the way it is written or spoken.

Question 2

What are the two levels of Linguistic Analysis?

Answer 2

Syntax: What part of the given text is grammatically correct

Semantics: What is the meaning of the given text

Question 3

What is Natural Language Understanding?

Answer 3

Trying to understand the meaning of the given text

Question 4

What are the four ambiguities that need to be resolved for NLU?

Answer 4

Lexical, Syntactic, Semantic, Anaphoric

Question 5

What is Lexical Ambiguity?

Answer 5

Words have multiple meaning, also known as Polysemy or Synonomy

Question 6

What is Syntactic Ambiguity?

Answer 6

A sentence has multiple parse trees

Question 7

What is Semantic Ambiguity?

Answer 7

Sentence has multiple meanings.

Question 8

What is Anaphoric Ambiguity?

Answer 8

One word or phrase has two different meanings in the sentence.

Question 9

What are the four steps in the NLU process?

Answer 9

Syntax Analysis, Semantics, Named Entity Recognition, intent Recognition.

Question 10

What are the 7 steps in the NLP Pipeline?

Answer 10

Sentence Segmentation, Tokenization, Stemming, Part of Speech tagging, parsing, Named Entity Recognition, Co-reference (discourse) resolution.

Question 11

What is Sentence Segmentation?

Answer 11

The process of Identifying the sentence boundaries in the text.

Question 12

What is Tokenization?

Answer 12

The process of Identifiying different words, numbers, and other punctuations

Question 13

What is Stemming?

Answer 13

The process of stripping the ends of words.

Question 14

What is Part of Speech (POS) Tagging?

Answer 14

The process of assigning each word in a sentence its own part of speech tag such as designating words as nouns or verbs.

Question 15

What is Parsing?

Answer 15

The process of dividing given sentences into different categories.

Question 16

What is Named Identity Recognition?

Answer 16

The process of Identifying entities such as a person, location, or time.

Question 17

What is Co-Reference (Discourse) Resolution?

Answer 17

The process of defining the relationship of an given word in the sentence with the next and previous sentence.

Question 18

What is the goal of Lemmatization and Stemming?

Answer 18

The goal is to reduce the inflectional forms and derivationally related forms of a word to a common base form

Question 19

What is the difference between Lemmatization and Stemming?

Answer 19

Stemming is a crude heuristic process that just chops the end of the word off, whereas lemmatization does it properly with the use of a vocabulary and morphological analysis of words.

Question 20

What are stop words?

Answer 20

A list of the most common words in a language. This list is not universal and can change depending on application.

Question 21

What is a “Bag-of-Words”?

Answer 21

A simple feature extraction techniques that describes the occurrence of each word in a document with no care for location information. The idea is that similar documents have similar contents.

Question 22

What is Term Frequency-Inverse Document Frequency (TF-IDF)?

Answer 22

This is a statistical measure used to evaluate the importance of a word to a document or in a collection.

Question 23

What is N-gram word prediction?

Answer 23

Using the probabilities of a sequence of words to choose the most likely next word or provide correction of spelling errors.

Question 24

What is the Markov Assumption for Language?

Answer 24

Only prior local context, the last few words, affects the next word. This means that the probability of a word only depends on the previous N-1 words.

Question 25

What are the limitations of the N-gram model?

Answer 25

The higher the N the better the model overall but this leads to a lot of computational overhead.

N-grams are a sparse representation of a language

It will be a 0 probability to all words that are not in the training corpus.