Natural Language Processing - Exam Lecture 0
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Questions and Answers

What are some of the challenges that make language hard in the context of natural language processing?

Tremendous ambiguity, variability, grounding, and sparsity are some of the challenges that make language hard in NLP.

How does Zipf's Law apply to language and what does it reveal about the distribution of words?

Zipf's Law reveals that few words make up a great share of the text, with the second most frequent word appearing half as much as the first, the third one appearing one fourth as much, and so on.

What disciplines does NLP draw upon for its foundation?

NLP draws upon Human & Social Sciences, Linguistics, and Cognition & Neuroscience for its foundation.

How does the variability of language pose a challenge in NLP?

<p>The variability of language, including semantic change over years, presents a challenge in understanding the meaning of words and expressions.</p> Signup and view all the answers

What role does grounding in common sense play in the challenge of learning language?

<p>Grounding in common sense is essential, as humans don't learn language by observing an endless stream of text but rely on common sense to understand and interpret language.</p> Signup and view all the answers

What is the difference between Extractive Summarization and Abstractive Summarization?

<p>Extractive Summarization interprets input and generates summary in a more human-like manner, while Abstractive Summarization selects a subset of sentences from the input text to form a summary, typically the most informative sentences from the original.</p> Signup and view all the answers

What is the objective of text normalization in NLP?

<p>The objective of text normalization is to harmonize written forms of words with the same meaning, standardize text for easier processing, and reduce language fluctuation.</p> Signup and view all the answers

Why are numbers and dates usually replaced by special tokens in NLP preprocessing?

<p>Numbers and dates are usually replaced by special tokens in NLP preprocessing to prevent the dictionary from exploding, as their inclusion may significantly increase the size of the dictionary.</p> Signup and view all the answers

What is the purpose of stemming in NLP?

<p>The purpose of stemming in NLP is to reduce words to their stems by crude affix chopping, aiming to capture the core meaning of words.</p> Signup and view all the answers

How does lemmatization differ from stemming in NLP?

<p>Lemmatization uses knowledge resources like WordNet to find and replace base forms, reducing inflectional/variant forms to base forms, while stemming typically involves faster reduction of variation but may harm precision and increase ambiguity.</p> Signup and view all the answers

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