Networking lesson 4 chaoter 1 Module 4-1.pptx
- 2. Natural Language Processing (NLP) Natural Language Processing (NLP) refers to AI method of communicating with an intelligent systems using a natural language such as English. Processing of Natural Language is required when you want an intelligent system like robot to perform as per your instructions, when you want to hear decision from a dialogue based clinical expert system, etc. The field of NLP involves making computers to perform useful tasks with the natural languages humans use. The input and output of an NLP system can be: Speech Written Text
- 3. Natural language processing (NLP) is a machine learning technology that gives computers the ability to interpret, manipulate, and comprehend human language.
- 4. Components of NLP There are two components of NLP as given: 1.Natural Language Understanding (NLU) Understanding involves the following tasks: Mapping the given input in natural language into useful representations. Analyzing different aspects of the language. 2.Natural Language Generation (NLG) It is the process of producing meaningful phrases and sentences in the form of natural language from some internal representation. It involves: Text planning: It includes retrieving the relevant content from knowledge base. Sentence planning: It includes choosing required words, forming meaningful phrases, setting tone of the sentence. Text Realization: It is mapping sentence plan into sentence structure. The NLU is harder than NLG.
- 6. Basic Concepts in Natural Language Processing (NLP) Natural Language Processing (NLP) is a subfield of artificial intelligence that focuses on the interaction between computers and humans through natural language. Here are some fundamental concepts: 1. Tokenization •Definition: The process of breaking down text into smaller units, such as words or phrases (tokens). •Purpose: Facilitates the analysis and processing of text by treating tokens as individual elements. 2. Part-of-Speech Tagging •Definition: Assigning parts of speech (noun, verb, adjective, etc.) to each token in a sentence. •Purpose: Helps in understanding the grammatical structure and meaning of sentences.
- 7. 3. Named Entity Recognition (NER) •Definition: Identifying and classifying key entities in text, such as names of people, organizations, locations, and dates. •Purpose: Extracts valuable information from unstructured text for further analysis. 4. Sentiment Analysis •Definition: Determining the sentiment or emotional tone behind a piece of text (positive, negative, or neutral). •Purpose: Useful for analyzing customer feedback, social media posts, and product reviews. 5. Text Classification •Definition: Assigning predefined categories to text documents based on their content. •Purpose: Enables organization and retrieval of information, such as spam detection in emails.
- 8. 6. Machine Translation •Definition: Automatically translating text from one language to another using algorithms. •Purpose: Facilitates communication across language barriers. 7. Language Modeling •Definition: Predicting the probability of a sequence of words. Models can be used for text generation, autocomplete, etc. •Purpose: Helps systems understand language context and generate coherent text. 8. Word Embeddings •Definition: Representations of words in continuous vector space, capturing semantic meanings and relationships. •Purpose: Enhances machine learning models' understanding of word similarities and contextual meanings (e.g., Word2Vec, GloVe).
- 9. Text Processing Techniques Text processing involves various methods and techniques used to analyze and manipulate textual data. Here are some common text processing techniques used in natural language processing (NLP): 1. Text Normalization •Definition: The process of transforming text into a standard format. •Techniques: • Lowercasing: Converting all characters to lowercase to ensure uniformity. • Removing Punctuation: Eliminating punctuation marks to focus on words. • Removing Stop Words: Filtering out common words (e.g., "the," "is," "in") that may not contribute significant meaning.
- 10. 2. Tokenization •Definition: Splitting text into individual units (tokens), such as words, phrases, or sentences. •Types: • Word Tokenization: Breaking text into words. • Sentence Tokenization: Dividing text into sentences. •Purpose: Facilitates further analysis and processing of text. .
- 11. 3. Stemming and Lemmatization •Stemming: • Definition: Reducing words to their base or root form, often by removing suffixes (e.g., "running" to "run"). • Algorithm: Common algorithms include the Porter Stemmer and Snowball Stemmer. •Lemmatization: • Definition: Similar to stemming, but it reduces words to their dictionary form (lemma) while considering the context (e.g., "better" to "good"). • Tools: Often requires a vocabulary and morphological analysis
- 12. 4. Part-of-Speech Tagging (POS Tagging) •Definition: Assigning parts of speech to each token in a sentence (e.g., noun, verb, adjective). •Purpose: Helps in understanding the grammatical structure and relationships within text. 5. Named Entity Recognition (NER) •Definition: Identifying and classifying named entities (e.g., people, organizations, locations) in text. •Purpose: Extracts structured information from unstructured text, useful for information retrieval. 6. Text Classification •Definition: Assigning predefined categories to text based on its content. •Applications: Spam detection, sentiment analysis, topic categorization. •Techniques: Can use machine learning algorithms like Naive Bayes, SVM, or deep learning approaches.
- 13. Advanced Natural Language Processing (NLP) techniques involve sophisticated methods and models that are used to analyze, understand, and generate human language. These techniques have evolved significantly over time, leveraging advances in machine learning, deep learning, and large-scale datasets Advanced NLP Technique
- 14. Word embedding (Word2Vec, GloVe) Word embedding are a type of word representation that enables words to be represented as vectors in a continuous vector space. This allows for capturing the semantic meaning of words and their relationships based on context. Two popular methods for generating word embedding are 1. Word-2Vec and 2. GloVe.
- 15. 1. Word2Vec •Developed by a team led by Tomas Mikolov at Google in 2013. •Utilizes neural networks to create word embeddings based on the context of words in a corpus. Key Features: •Training Methods: • Continuous Bag of Words (CBOW): Predicts a target word based on its context (surrounding words). • Skip-Gram: Predicts surrounding words given a target word. This method is particularly effective for infrequent words. •Vector Representation: Each word is mapped to a unique vector in a high-dimensional space (usually 100-300 dimensions). •Semantic Relationships: Captures relationships such as synonyms and analogies. For example, the vector relationship can represent "king" - "man" + "woman" ≈ "queen". Applications: •Text classification, sentiment analysis, and any task requiring an understanding of word
- 16. 2. GloVe (Global Vectors for Word Representation) •Developed by researchers at Stanford, GloVe was introduced in 2014. •Focuses on capturing global statistical information of the corpus to create embeddings. Key Features: •Matrix Factorization: GloVe constructs a word co-occurrence matrix that counts how often words appear together in a corpus. Each entry in the matrix represents the frequency with which word jjj appears in the context of word iii. •Objective Function: The embeddings are learned by factorizing this co-occurrence matrix, optimizing a weighted least squares objective that captures the relationships between the words. •Interpretability: GloVe vectors tend to be more interpretable and can show strong linear relationships between words. Applications: •Similar to Word2Vec, GloVe is used in various NLP tasks, including information retrieval, word similarity tasks, and as input features for machine learning models.
- 17. Transformer Models Transformer models are a class of deep learning models that have revolutionized the field of Natural Language Processing (NLP) and are widely used for a variety of tasks like machine translation, text generation, question answering, and more. Introduced in the seminal paper "Attention is All You Need" by Vaswani et al. (2017), the transformer architecture relies heavily on self-attention mechanisms rather than traditional recurrent neural networks (RNNs) or convolutional neural networks (CNNs).
- 18. Key Concepts of Transformer Models •1.Self-Attention Mechanism: •Function: Allows the model to weigh the importance of different words in a sentence relative to each other. Each word can attend to every other word in the sequence, capturing contextual relationships. •Process: Computes attention scores using queries (Q), keys (K), and values (V), resulting in a weighted representation of the input. 2.Multi-Head Attention: •Description: Instead of having a single attention mechanism, transformers use multiple attention heads that allow the model to capture different types of relationships in the data. •Benefit: Enhances the model’s ability to focus on various aspects of the input simultaneously. 3.Positional Encoding: •Purpose: Since transformers do not inherently understand the order of words (unlike RNNs), positional encodings are added to input embeddings to provide information about the position of words in the sequence. •Method: Typically involves sine and cosine functions to generate unique encodings for each position. 3.Feedforward Neural Networks: •Structure: Each attention output is passed through a feedforward neural network, which applies linear transformations and non-linear activations, helping to process information further. •Layer Normalization and Residual Connections: •Purpose: To stabilize and enhance training, layer normalization is applied, and residual connections help to mitigate the vanishing gradient problem
- 19. Applications of NLP Natural Language Processing (NLP) has a wide range of applications that have revolutionized many industries by enabling computers to understand, interpret, and generate human language. Here are some of the key applications of NLP
- 20. Sentiment analysis, chat-bots, machine translation 1. Machine Translation Description: Machine translation involves automatically converting text from one language to another. This is one of the most common applications of NLP, widely used in tools like Google Translate. Examples: Google Translate: Translates text or speech between multiple languages. DeepL: Known for its high-quality translations that rival human translation in some cases. 2. Sentiment Analysis Description: Sentiment analysis involves determining the sentiment expressed in a piece of text (e.g., positive, negative, or neutral). This is particularly useful for analyzing customer feedback, social media content, and brand reputation. Examples: Social Media Monitoring: Analyzing tweets, reviews, or comments to gauge public opinion. Customer Support: Automatically analyzing customer complaints or feedback to detect satisfaction levels or concerns
- 21. Chatbots and Conversational Agents •Description: NLP is essential for creating intelligent chat-bots and virtual assistants that can engage in natural, human-like conversations with users. •Examples: •Customer Service Chat-bots: Automating customer service tasks, such as answering questions, troubleshooting, and providing product recommendations. •Voice Assistants: Tools like Amazon Alexa, Apple Siri, and Google Assistant use NLP to understand voice commands and provide relevant responses.
- 22. End Any question