Natural Language Processing Overview

Natural Language Processing (NLP) is a field of AI focused on enabling computers to understand, interpret, and manipulate human language.

Text preprocessing involves preparing text data for NLP tasks by cleaning, normalizing, and structuring it. This includes tasks like removing punctuation, converting to lowercase, and stemming words.

Information retrieval involves retrieving relevant information from large sets of text data based on user queries. It aims to find the most pertinent documents or information pieces.

Information extraction aims to extract specific data from text, including keywords, entities, relationships, and topics. This data can be used for various purposes, like building knowledge graphs.

Named Entity Recognition (NER) is a sub-task of information extraction that identifies and classifies named entities in text, like person names, locations, and organizations.

Sentiment analysis is a technique used to understand the emotional tone or polarity (positive, negative, neutral) of text data. It helps gauge public opinion, customer feedback, and brand perception.

Text mining involves analyzing large amounts of text data to extract meaningful insights, patterns, and relationships. It can be used for tasks like market research, topic discovery, and trend analysis.

Generative AI refers to algorithms that can create new content, like text, images, or code. It leverages deep learning models to generate novel output based on patterns learned from training data.

A branch of computer science that focuses on enabling computers to understand, interpret, and generate human language.

Rule-based NLP approaches relied on strict predefined rules, like grammar rules, to analyze and process text. They required expert knowledge and were often rigid, lacking adaptability.

Statistical NLP uses statistical models and machine learning to analyze text based on data patterns. This approach is more flexible and adaptable to variations in language.

Deep learning approaches leverage complex neural networks and large data sets to achieve highly accurate language processing. They excel in tasks like translation and text generation.

The process of preparing text data for NLP tasks. It involves tasks like removing unwanted characters, converting text to lowercase, and splitting text into individual words.

Words or phrases that are commonly used but often carry little meaning for information retrieval. Examples include "the", "a", "an", and "is".

Reducing words to their base form by removing suffixes. For example, "running" and "ran" are both reduced to "run".

A sequence of consecutive words. For example, "big data" is a 2-gram and "natural language processing" is a 4-gram. N-grams help understand word co-occurrence and context.

A technique used in natural language processing (NLP) to break down text into smaller units called tokens. These tokens can be individual words, characters, or even subwords, depending on the chosen method.

A type of tokenization that splits text into individual words, creating a list of words in the text.

A tokenization method that splits text into individual characters, treating each letter or symbol as a separate token.

A type of tokenization where text is broken down into meaningful subwords or parts of words. This is often used in advanced language models like BERT and GPT.

A process that analyzes text to determine the overall sentiment or emotion expressed, such as positive, negative, or neutral. It is useful in understanding customer feedback, analyzing social media sentiment, and gauging public opinion.

Artificial intelligence systems designed to engage in conversations with humans. Chatbots are trained on large datasets of text and code to understand natural language input and generate human-like responses.

The process of automatically translating text from one language to another. It involves complex algorithms that analyze the source language and generate the equivalent meaning in the target language.

The process of generating concise summaries of longer pieces of text, such as articles, research papers, or reports. This helps users quickly understand the key points and main ideas of the text.

Large language models (LLMs) are capable of picking up on patterns and learning to identify named entities with a few examples, making them powerful tools for specific tasks.

A prompt provides a set of input-output pairs that illustrate the task at hand, allowing the LLM to learn from these examples during a specific interaction.

Unlike traditional model training, ICL doesn't permanently update the LLM's knowledge base. The 'learned' information only persists within the current conversation and doesn't carry over to future interactions.

A tagset is a collection of symbols or labels used to annotate parts of speech (POS) in text. For example, the Treebank tagset uses 'NN' for nouns, 'VB' for verbs, and 'JJ' for adjectives.

Accuracy is a common metric for assessing classification results and is simply calculated as the proportion of correctly classified examples

Precision measures the proportion of correctly identified positive examples out of all examples identified as positive (true positives + false positives). It indicates how well the model avoids false positives.

Recall, also known as sensitivity, measures the proportion of correctly identified positive examples out of all actual positive examples (true positives + false negatives). High recall implies that the model correctly identifies most of the positive cases.

The F-score, specifically the F1 score, represents a balanced measure of precision and recall, taking their harmonic mean. It's a useful overall metric for classification tasks.

A mathematical operation that measures the similarity between two vectors (query vector and document vector). High scores indicate a strong match.

It represents a weighted average of a document's terms based on their frequency and rarity. It favors documents with frequent occurrences of rare query terms.

A measure of a document's length and the number of query terms it contains. Longer documents with the same number of query terms are penalized.

A weighting scheme that combines the importance of a term within a document (Term Frequency) with the inverse document frequency (IDF). It highlights terms that are both frequent in a document and rare across a collection of documents.

A collection of command-line tools for working with PDF documents, built upon the Poppler rendering library.

A Python library that generates sentence and text embeddings, utilizing transformer models.

Dense vector representations of sentences or paragraphs, capturing their semantic meaning.

The ability to compare the similarity between sentences or paragraphs based on their semantic meaning.

In information retrieval, document weight vectors are used to represent the importance of different terms in a document. Each term is assigned a weight based on its frequency and significance in the document, creating a vector that captures the document's overall content.

The dot product is a mathematical operation that calculates the similarity between two vectors. In information retrieval, it's used to compare a query vector (representing the user's search terms) with document weight vectors, determining document relevance.

Term frequency (TF) is a measure of how often a term appears in a document. It's a key factor in determining the importance of a term within a document.

TF convexity refers to the idea that the more a term occurs in a document, the less additional weight it should receive. This ensures that very frequent terms don't dominate the document representation.

Information retrieval (IR) aims to retrieve relevant information from large sets of data based on user queries. It's about finding the most pertinent documents or information pieces.

A machine can evaluate the quality of a document by analyzing its content and structure. This may include factors like term frequency, document length, and the presence of specific keywords.

Longer documents often contain more information, which can be helpful for comprehensive searches. However, they can also be overwhelming and difficult to process.

A good document is one that is relevant, accurate, up-to-date, and easy to understand. These criteria can be evaluated by machines through various techniques, like analyzing the text and its structure.