Natural Language Processing Basics

Questions and Answers

What is the primary use of N-grams?

To use contiguous sequences of words in a document (correct)

To analyze individual words only

To synthesize new documents

To create word clouds

N-grams only consider the previous words to determine the next word in a sequence.

False

What technique is used to train word embeddings based on relationship values between words?

Machine learning approaches

Dependency parsing uses a ______ to represent the relationship of each word with other words.

graph Signup and view all the answers

Match the following concepts with their descriptions:

N-grams = Contiguous sequences of words Dependency Parsing = Relationship representation as a graph Word Embeddings = Embedding vectors based on word relationships TF-IDF = Term weighting for document relevance Signup and view all the answers

Which of the following describes TF-IDF?

It combines term frequency with document frequency. Signup and view all the answers

Word vectors are a representation of words in high-dimensional space.

True Signup and view all the answers

What does N-grams analysis primarily rely on?

Contiguous sequences of words Signup and view all the answers

In machine learning, n-grams help in creating ______ for various natural language processing tasks.

features Signup and view all the answers

What type of relationships do word embeddings typically represent?

Related and unrelated word contexts. Signup and view all the answers

Study Notes

Natural Language Processing (NLP)

NLP focuses on the interaction between computers and humans through natural language.
Essential to model and represent human language effectively in computer systems.

Challenges in NLP

Language ambiguity: A single phrase can have multiple interpretations, complicating machine understanding.
Evolving language: New slang and expressions emerge, necessitating constant updates to fixed language models.
Context-dependent meanings: Words can change meaning based on context, as seen with the phrase "drastically decreased," which can be positive or negative.
Sarcasm and figures of speech further complicate interpretation. For example, praising something can be used sarcastically, altering its intended meaning.

Preprocessing in NLP

Preprocessing prepares raw text data for modeling in NLP.
Part-of-Speech (POS) Taggers analyze and categorize each word's role (e.g., noun, verb).

Data Modelling

Represents observations or instances in text data as lists of words, each linked with relevant information.
Word embeddings transform words into mathematical representations, enabling various mathematical operations.

Types of Word Embeddings

Frequency-based encoding: Focuses on the most frequently occurring words in a dataset.
Prediction-based encoding: Uses context to predict word placements.

Naïve Word Embedding

Constructs a dictionary of unique words within a document.
Each word can be expressed as a one-hot encoded vector, indicating its position in the dictionary.

N-Grams

Utilizes contiguous sequences of words from a text for analysis, with varying lengths (n) based on the analysis task.
Can utilize previous words or examine relationships bi-directionally to enhance understanding.

Dependency Parsing

Represents the grammatical structure of sentences using a graph, demonstrating the relationships between words.
Machine learning techniques can enhance the effectiveness of dependency parsing.

Description

This quiz introduces the fundamental concepts of Natural Language Processing (NLP). Explore the challenges posed by human language, such as ambiguity and evolving meanings. Gain insights into how computers interact with human language effectively.