Weekairtificial intelligence 8-Module 7 NLP.pptx

ppt for artificial intelligence for simple study

1. Module 7 Natural Language Processing

2. Natural Language Processing (NLP)

3. Applications of NLP

4. Cont... Sentiment Analysis: Extracting opinions and emotions from text data [e.g., social media monitoring]. Text Summarization: Condensing large amounts of text into key points. Autocorrect and Predictive Text: Suggesting corrections and completions as you type. Spam Filtering: Identifying and blocking unwanted emails. Search Engines: Ranking search results based on relevance to your query.

5. Challenges in Processing Human Language

6. Cont... Slang and Informal Language: Keeping up with ever-evolving slang and informal language usage. Incomplete Sentences and Utterances: Human conversation often involves shortcuts and missing information that can be confusing for machines. NLP researchers are constantly developing techniques to address these challenges and improve the accuracy and robustness of NLP systems.

7. Key NLP Tasks Here's a glimpse into some fundamental NLP tasks that form the building blocks for many applications: Tokenization: Breaking down text into smaller units like words, punctuation marks, or phrases. Part-of-Speech (POS) tagging: Identifying the grammatical function of each word in a sentence (e.g., noun, verb, adjective). Named Entity Recognition (NER): Recognizing and classifying named entities in text, such as people, organizations, locations, dates, monetary values, etc.

8. 1. Tokenization: Imagine you're dissecting a sentence. Tokenization is the first step, where you break the sentence down into its individual building blocks. These blocks can be: Words: "The", "quick", "brown", "fox" Punctuation marks: ".", ",", "?" Sometimes even phrases: "New York City" (depending on the application)

9. 2. POS Tagging:

10. 3. Named Entity Recognition (NER): This focuses on identifying and classifying specific entities within the tokens. Imagine circling important names on a page. NER does something similar, recognizing entities like: People: "Albert Einstein" Organizations: "Google" Locations: "Paris" Dates: "July 4th, 2024" Monetary values: "$100"

11. Practical Examples

12. 2. Social Media Analysis:

13. 3. Spam Filtering:

14. 4. Machine Translation: Tokenization: Breaking down a sentence in one language (e.g., Spanish) into individual words. POS Tagging: Identifying the grammatical function of each word to understand the sentence structure. NER: Recognizing named entities to ensure accurate translation within the context. These tasks work together for the translation engine to understand the original sentence's meaning and produce a grammatically correct and meaningful translation in the target language.

15. Text Cleaning and Normalization for NLP Text data often comes in a raw and messy format. It can contain inconsistencies, irrelevant information, and variations in how words are written. Cleaning and normalization are crucial steps in NLP to prepare the text for further processing. Here's a breakdown of some common techniques:

16. 1. Removing Stopwords: Stopwords are very common words that carry little meaning on their own (e.g., "the", "a", "is").  Removing them can improve processing efficiency and focus the analysis on more content-rich words.

17. 2. Removing Special Characters: Punctuation marks, symbols, and emojis can add noise to the data. Depending on the task, you might choose to remove them entirely or convert them to a standard format.

18. 3. Lowercasing/Uppercasing: Text data can be written in different cases (uppercase, lowercase). Converting everything to lowercase or uppercase ensures consistency and simplifies further processing.

19. 4. Normalizing Text: This can involve: Expanding Abbreviations: Converting abbreviations to their full forms (e.g., "e.g." to "for example"). Handling Emojis: Converting emojis to text descriptions or removing them altogether. Handling Numbers: Converting numbers to text (e.g., "2023" to "two thousand twenty-three") or leaving them as numerals depending on the task.

20. 5. Lemmatization vs. Stemming:

21. Cont... The choice between lemmatization and stemming depends on your specific application.  Lemmatization is generally preferred for tasks where preserving meaning and grammatical accuracy is crucial. Stemming can be faster and sufficient for simpler tasks where the exact meaning of the base form isn't critical.

22. Additional Considerations Text Normalization Libraries: Libraries like NLTK (Python) and spaCy (Python) offer functionalities for many of these text cleaning and normalization tasks. Context-Specific Normalization: The specific techniques you apply might vary depending on your NLP task and the nature of your text data. Trade-offs: There can be trade-offs between cleaning too aggressively and losing information, and cleaning too lightly and introducing noise. Finding the right balance depends on your specific needs.

23. Some of the examples

24. 1. Social Media Sentiment Analysis: Imagine analyzing tweets to understand public sentiment towards a new product launch. You'd want to clean the text by: Removing stopwords : Words like "a", "the", "is" don't contribute much to sentiment. Removing special characters: Emojis, hashtags, and punctuation can be removed or converted for consistency. Lowercasing: Case variations shouldn't affect sentiment analysis. Normalizing slang and abbreviations: "OMG" could be converted to "oh my god" for better understanding.

25. 2. Web Scraping and Text Summarization:

26. 3. Chatbot Development:

27. 4. Machine Translation:

28. 5. Text Classification:

29. 1. Bag-of-Words (BoW) Model: Concept: BoW is a simple way to represent documents as numerical vectors. Process: Each document is treated as a "bag" of words, ignoring the order and grammar of the words. A vocabulary of unique words is created across all documents in the corpus. Each document is represented by a vector where each element corresponds to a word in the vocabulary. The value of each element indicates the frequency (count) of the corresponding word appearing in that document.

30. Example: Document 1: "The cat sat on the mat." Document 2: "The dog chased the cat." Vocabulary: {the, cat, sat, on, mat, dog, chased} Document 1 vector: [3, 1, 1, 1, 1, 0, 0] (3 occurrences of "the", etc.) Document 2 vector: [2, 1, 0, 0, 0, 1, 1]

31. Limitations:

32. 2. Term Frequency-Inverse Document Frequency (TF-IDF): Concept: TF-IDF builds upon BoW but considers the importance of words within a document and across the entire corpus. Process: TF (Term Frequency) for a word in a document is calculated as its count divided by the total number of words in that document. IDF (Inverse Document Frequency) for a word is calculated as the logarithm of the total number of documents in the corpus divided by the number of documents containing that word. High IDF means the word is less frequent across documents and potentially more informative. The TF-IDF weight for a word is then calculated by multiplying TF and IDF.

33. Benefits:

34. 3. Word Embeddings and Distributed Representations (Word2Vec, GloVe ):

35. Benefits:

36. 4. Language Models and Pre-trained Transformers:

37. Benefits:

38. Here's an analogy:

39. Understanding Sentiment Analysis

40. Techniques: Lexicon-based approach: Uses pre-defined dictionaries of words with positive, negative, and neutral sentiment scores. The overall sentiment is calculated based on the sentiment scores of the words in the text. Machine learning: Trains models on labeled data (text with known sentiment) to automatically classify new text. Popular algorithms include Naive Bayes, Support Vector Machines (SVM), and Logistic Regression. Deep learning: Utilizes neural networks like Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks to capture complex relationships between words and improve sentiment classification accuracy.

41. Building Sentiment Analysis Models

42. 2. Feature Engineering:

43. 3. Model Training:

44. 4. Evaluation:

45. Interpreting Sentiment Analysis Results

46. Cont.. Use the results as an indicator of overall sentiment but don't rely solely on them for drawing definitive conclusions. Analyze the data with a critical eye and consider the context in which the text was written.

47. Theoretical Explanation

48. 1. Introduction to Topic Modeling and Latent Dirichlet Allocation (LDA) Topic modeling is a statistical method for uncovering hidden thematic structures within a collection of documents. It aims to identify groups of words (topics) that frequently appear together and describe the main subjects discussed in the corpus.

49. Cont... Latent Dirichlet Allocation (LDA) is a popular topic modeling algorithm. Here's the basic idea: Each document is assumed to be a mixture of various topics in different proportions. Each topic is represented by a probability distribution over words in the vocabulary. LDA analyzes the documents in a corpus and tries to discover these underlying topics and their distribution across documents.

50. 3. Evaluating Topic Models and Selecting the Optimal Number of Topics There's no single "best" number of topics for LDA. Here are some approaches to guide your selection: Perplexity: LDA calculates perplexity, a measure of how well the model fits unseen data. Lower perplexity often indicates a better fit. However, it can be sensitive to model parameters. Topic Coherence: Evaluate how well the words within a topic are semantically related. Various metrics like coherence score (CoherenceModel in Gensim) can help assess this. Domain Knowledge: Consider your understanding of the domain and the expected number of relevant themes within the documents.

51. 4. Introduction to Text Generation Techniques

52. 2. Recurrent Neural Networks (RNNs):