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Questions and Answers
What does lexical ambiguity refer to in the context of NLP?
Which of the following represents a challenge in Natural Language Processing?
What is the primary purpose of tokenization in text preprocessing?
What is the purpose of POS (part of speech) tagging in NLP?
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Which of the following techniques is used for reducing words to their base form without losing meaning?
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Which of the following is an example of semantic ambiguity?
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What is the main drawback of stemming in text normalization?
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What are the initial steps in an NLP implementation pipeline?
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Which of the following methods falls under the category of feature engineering?
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Which of the following best describes syntactic ambiguity?
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What is a prime example of a solution for ambiguity in NLP?
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Which model building approach uses Decision Trees?
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What do stop words removal, punctuation removal, and digit removal have in common?
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Which phrase best defines language in the context of NLP?
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Which vectorization technique represents words in a dense vector space?
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What is one disadvantage of lower casing in text preprocessing?
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What is the purpose of the split() function in Python?
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Which of the following functions is NOT a method provided by the 're' library for regular expressions in Python?
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What are stopwords and why are they removed from text?
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What is the main function of a grammar in programming languages?
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In which scenario is it useful to remove contractions in text data?
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Which of the following is NOT a component of the formal definition of a grammar G?
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What does part-of-speech tagging accomplish in text processing?
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How does lemmatization differ from stemming?
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Which library is commonly used in Python for stopword removal?
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Which of the following accurately describes in-order traversal of a parse tree?
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Which of the following operations is performed by the re.sub() function in regex?
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What is the purpose of the output.draw()
function in the context described?
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What is the main benefit of using regular expressions (RegEx) for tokenization?
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What is the main purpose of syntactic analysis in parsing?
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Which of the following best describes top-down parsing?
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Which of the following is a disadvantage of recursive descent parsing?
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What does a parse tree represent?
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In left-most derivation, how is the sentential form scanned?
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What role does a parser component primarily serve?
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What type of parsing starts with input symbols and constructs the parse tree upward?
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In the context of parsing, what are terminal nodes in a parse tree?
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Study Notes
Syllabus and Evaluation
- Evaluation includes Unit Tests for Units 1 & 2, Research Seminar for Unit 3, and a Quiz for Units 4 & 5.
- Communication mode is through Moodle.
NLP Certificate Courses
- Natural Language Processing Specialization - Coursera
- Become a Natural Language Processing Expert - Udacity
- Introduction to Natural Language Processing in Python - DataCamp
- NLP with Deep Learning in Python - Udemy
- NLP in TensorFlow - Coursera
- Additional offerings from Codecademy and Edureka.
Understanding Natural Language Processing (NLP)
- Language is a structured communication system made up of characters, words, and sentences.
- Linguistics informs the study of NLP and its structural aspects.
NLP Phases
- Text Preprocessing consists of cleaning, tokenization, stop word removal, normalization, and vectorization.
- Model Building strategies include heuristics, machine learning (e.g., Naïve Bayes, SVM), and deep learning (e.g., CNN, RNN).
Challenges in NLP
- Contextual interpretation of words can lead to lexical, semantic, and syntactic ambiguities.
- Ambiguity examples demonstrate the difficulty in human interpretation without context.
Text Preprocessing Techniques
- Text Cleaning: Involves removing HTML tags, emojis, and correcting spelling.
-
Tokenization: Divides text into smaller units (tokens); methods include using Python’s
split()
function and regex. - Stop Word Removal: Frequent words like "the", "is", and "at" are eliminated to enhance meaningful analysis.
- Case Normalization: Text is converted to a uniform case, usually lower, sometimes losing context-sensitive information.
Stemming and Lemmatization
- Stemming: Reduces words to their root form but might lose meaning (e.g., "programmer" to "program").
- Lemmatization: Similar to stemming but ensures that the base form retains its context and meaning.
Part-of-Speech (POS) Tagging
- Assigns grammatical tags to words (e.g., noun, verb, adjective), crucial for applications like text-to-speech and word sense disambiguation.
Syntactic Parsing
- Involves analyzing sentence structure for grammatical correctness, rejecting nonsensical constructions (e.g., “hot ice-cream”).
- Types of Parsing include:
- Top-down Parsing: Starts from the start symbol and derives downwards.
- Bottom-up Parsing: Begins with the input symbols and works upwards.
Grammar in NLP
- Grammar, based on Chomsky’s model, describes the syntactic structure of languages through formal tuples comprising non-terminal and terminal symbols, a start symbol, and production rules.
Semantic Parsing
- Involves deriving meaning from text while adhering to grammatical rules, ensuring clarity and interpretive accuracy.
Special NLP Techniques
- Decontracting Words: Expands contractions to standardize text for better analysis.
- Punctuation Removal and Lowering Text: Enhances uniformity but may potentially result in lost context, depending on analysis goals.
Practical Applications
- NLP can enhance various applications, including information retrieval, chatbot creation, text analysis, and sentiment detection.
Conclusion
- Mastery of NLP requires understanding its phases, challenges, preprocessing techniques, and application methods for effective implementation in real-world scenarios.
Studying That Suits You
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Description
Test your knowledge on Natural Language Processing with this quiz covering Units 4 and 5 of the syllabus. This quiz is designed to assess your understanding of advanced NLP concepts and techniques. Prepare yourself with reference books and tutorials to excel!