NLP note.pdf

Prepared by :Ayesha Arab Natural Language Processing CHP 1 :INTRODUCTION TO NLP Introduction to Natural Language Processing (NLP) Define: Natural Language Processing (NLP) is about teaching computers to understand and process...

Prepared by :Ayesha Arab Natural Language Processing CHP 1 :INTRODUCTION TO NLP Introduction to Natural Language Processing (NLP) Define: Natural Language Processing (NLP) is about teaching computers to understand and process human language. Unlike structured data (like spreadsheets), human language is unstructured and complex, making it challenging for computers to interpret accurately. NLP aims to bridge this gap by making sense of textual and spoken data. Key Steps in NLP 1. Sentence Segmentation o What It Is: Breaking text into individual sentences. o Example: ▪ Input: "San Pedro is a town... It is the second-largest town..." ▪ Output: "San Pedro is a town... It is the second-largest town..." 2. Word Tokenization o What It Is: Splitting sentences into words (tokens). o Example: ▪ Input: "San Pedro is a town..." ▪ Output: ['San', 'Pedro', 'is', 'a', 'town', '...'] 3. Predicting Parts of Speech (POS) o What It Is: Identifying if each word is a noun, verb, adjective, etc. o Example: ▪ Input: "Town is large." ▪ Output: 'Town' – Noun, 'is' – Verb, 'large' – Adjective 4. Lemmatization o What It Is: Reducing words to their root forms. o Example: ▪ Input: "Buffaloes grazing..." ▪ Output: 'Buffalo' (root form) Prepared by :Ayesha Arab Natural Language Processing 5. Identifying Stop Words o What It Is: Filtering out common words like 'the', 'is', 'and' that don't add much meaning. o Example: Removing 'the', 'is' from "The cat is on the mat." 6. Dependency Parsing o What It Is: Analyzing the relationships between words in a sentence. o Example: ▪ Input: "San Pedro is a town..." ▪ Output: Parse tree with 'is' as the root, showing relationships. 7. Finding Noun Phrases o What It Is: Grouping related words that represent the same idea. o Example: ▪ Input: "Second-largest town in the Belize District" ▪ Output: Group 'second-largest town' as a noun phrase. 8. Named Entity Recognition (NER) o What It Is: Identifying and categorizing real-world entities like people, places, and organizations. o Example: ▪ Input: "San Pedro is a town in Belize." ▪ Output: 'San Pedro' – Geographic Entity, 'Belize' – Geographic Entity 9. Coreference Resolution o What It Is: Determining which words refer to the same entity. o Example: ▪ Input: "San Pedro is a town. It is in Belize." ▪ Output: 'It' refers to 'San Pedro' Summary NLP involves several steps to make sense of human language: Segmenting text into sentences and words. Classifying parts of speech. Prepared by :Ayesha Arab Natural Language Processing Reducing words to their root forms. Filtering out common words. Understanding word relationships. Grouping related words. Identifying real-world entities. Resolving references to the same entity. Text Pre-Processing in NLP Text pre-processing is a crucial step in preparing raw text for analysis or machine learning models. It involves cleaning and transforming the text to make it more manageable and suitable for processing. Here's a detailed explanation of some key text pre-processing techniques: 1. Regular Expressions (Regex) What It Is: Regular expressions (regex) are sequences of characters that define a search pattern. They are used for pattern matching within text. How It Works: Regex allows you to search for specific patterns in text, such as dates, phone numbers, or any other structured data. It can also be used to clean text by removing or replacing unwanted characters. Example: Pattern: \d+ o Meaning: Matches one or more digits. o Use: To find all numbers in a text. Pattern: \b\w+\b o Meaning: Matches any word boundary. o Use: To tokenize text by identifying words. Example Usage: Text: "The price is $45.67." Regex Pattern: \d+(\.\d{2})? o Matches: 45.67 o Use: Extract prices from text. 2. Tokenization Prepared by :Ayesha Arab Natural Language Processing What It Is: Tokenization is the process of splitting text into smaller units called tokens. These tokens can be words, phrases, or symbols. How It Works: Word Tokenization: Splits a text into individual words. o Example: "I love NLP" → ['I', 'love', 'NLP'] Sentence Tokenization: Splits a text into sentences. o Example: "I love NLP. It is fascinating." → ['I love NLP.', 'It is fascinating.'] Why It's Important: Tokenization simplifies text into manageable pieces, allowing further analysis like counting word frequencies or applying algorithms. 3. Stemming What It Is: Stemming is the process of reducing words to their base or root form. It removes prefixes and suffixes to get to the core meaning of a word. How It Works: Algorithm: Uses rules to strip affixes from words. Goal: To standardize different forms of a word. Example: Word: "running" o Stemmed Form: "run" Word: "fishing" o Stemmed Form: "fish" Why It's Important: Stemming helps in reducing the complexity of text data by consolidating different forms of a word into one, improving the effectiveness of text analysis and search. 4. Minimum Edit Distance What It Is: Minimum edit distance (also known as Levenshtein distance) measures the number of edits (insertions, deletions, or substitutions) needed to transform one string into another. How It Works: Algorithm: Calculates the smallest number of operations required to convert one string into another. Example Calculation: o String 1: "kitten" Prepared by :Ayesha Arab Natural Language Processing o String 2: "sitting" o Operations: ▪ Substitute 'k' with 's' ▪ Substitute 'e' with 'i' ▪ Insert 't' at the end ▪ Insert 'i' at the end ▪ Insert 'g' at the end ▪ Insert 's' at the end o Minimum Edit Distance: 3 Why It's Important: Edit distance helps in tasks like spell-checking, correcting typos, and matching similar strings by quantifying how close or different two strings are. Summary Regular Expressions: Pattern matching for extracting or cleaning specific text elements. Tokenization: Breaking text into smaller units for easier analysis. Stemming: Reducing words to their root form to standardize text. Minimum Edit Distance: Measuring the similarity between strings by counting edits needed to transform one into another. These pre-processing steps are fundamental in preparing text data for more advanced analyses and model training in NLP. FOR MINIMUM EDIT DISTANCE NUMERICAL YT REFEFENCE: Prepared by :Ayesha Arab Natural Language Processing CHP 2: N‐Gram Language Model INTRODUCTION TO N-GRAM An N-Gram is a sequence of NNN items (words or characters) from a text. The value of NNN determines the type of N-Gram: Unigram (1-Gram): Single item. o Example: ▪ Text: "The cat sat" ▪ Unigrams: ['The', 'cat', 'sat'] Bigram (2-Gram): Sequence of two items. o Example: ▪ Text: "The cat sat" ▪ Bigrams: ['The cat', 'cat sat'] Trigram (3-Gram): Sequence of three items. o Example: ▪ Text: "The cat sat" ▪ Trigrams: ['The cat sat'] 4-Gram: Sequence of four items. o Example: ▪ Text: "The cat sat on the mat" ▪ 4-Grams: ['The cat sat on', 'cat sat on the', 'sat on the mat'] N‐Gram probability estimation and perplexity Example Sentence: "Cats are cute" 1. Unigram Probability Unigram Probability is the probability of a single word occurring in a corpus. For the sentence "Cats are cute": Prepared by :Ayesha Arab Natural Language Processing Total Count of Words: 3 (i.e., "Cats", "are", "cute") Count of Each Word: o Count(Cats) = 1 o Count(are) = 1 o Count(cute) = 1 2. Bigram Probability Bigram Probability is the probability of a word occurring given the previous word. For the sentence "Cats are cute": Count of Bigram Pairs: o Count(Cats are) = 1 o Count(are cute) = 1 Count of Each Word: o Count(Cats) = 1 o Count(are) = 1 Prepared by :Ayesha Arab Natural Language Processing Summary Unigram Probability measures the likelihood of a single word occurring in the dataset. Bigram Probability measures the likelihood of a word occurring given the previous word. Trigram Probability measures the likelihood of a word occurring given the two preceding words. Prepared by :Ayesha Arab Natural Language Processing Step-by-Step Calculation of Perplexity Explanation: Probability Calculation: The probability of "Cats are cute" is 1.0, meaning the model perfectly predicts the sentence. Prepared by :Ayesha Arab Natural Language Processing Perplexity Calculation: Since the probability is 1.0, perplexity is also 1.0. Interpretation: A perplexity of 1.0 indicates that the model is very confident about the sentence and predicts it perfectly. In practical scenarios, a lower perplexity indicates better model performance, while a higher perplexity suggests the model is less confident or has poor predictive power. Smoothing technique(Laplace/good Turing/Kneser‐ Ney/Interpolation Laplace Smoothing, also known as Add-One Smoothing, is a technique used to handle the problem of zero probabilities in probabilistic models, particularly in language models. It ensures that no probability is zero, which is important when dealing with unseen words or word combinations. What is Laplace Smoothing? Laplace Smoothing adjusts the counts of n-grams by adding a small constant (usually 1) to all possible counts. This adjustment ensures that even previously unseen n-grams have a non-zero probability. How Laplace Smoothing Works For a given n-gram model, Laplace Smoothing modifies the probability calculation as follows: 1. Count of N-grams: o For each observed n-gram, add 1 to its count. o For each unobserved n-gram, assume a count of 1. 2. Probability Calculation: o Calculate the probability using the smoothed counts. Example: Bigram Model Text Corpus: "The cat sat on the mat." Vocabulary: {The, cat, sat, on, mat} 1. Without Smoothing Prepared by :Ayesha Arab Natural Language Processing Count of Bigrams: "The cat": 1 "cat sat": 1 "sat on": 1 "on the": 1 "the mat": 1 Total Count of Bigrams: 5 2. With Laplace Smoothing Adjust Counts: Add 1 to each observed bigram count. Assume a count of 1 for unseen bigrams. Vocabulary Size V=5V = 5V=5 (The, cat, sat, on, mat) Smoothed Counts: "The cat": 1 + 1 = 2 "cat sat": 1 + 1 = 2 "sat on": 1 + 1 = 2 "on the": 1 + 1 = 2 "the mat": 1 + 1 = 2 Prepared by :Ayesha Arab Natural Language Processing Unseen bigrams like "cat the": 0 + 1 = 1 Total Count of Bigrams (with smoothing): 5 (original) + 5 (for unseen bigrams) = 10 Summary Laplace Smoothing adjusts the counts by adding 1 to each observed bigram and assuming a count of 1 for unseen bigrams. Probability Calculation is updated to reflect these adjustments, ensuring that no bigram has a probability of zero. Turing Smoothing (Good-Turing Smoothing) Text Corpus: "The cat sat on the mat." Vocabulary: {The, cat, sat, on, mat} Concept: Good-Turing smoothing adjusts probabilities based on the frequency of counts. It estimates the probability of unseen bigrams by redistributing the probability mass of observed bigrams. Steps: 1. Count Frequencies: o Count of Bigrams (as before): "The cat": 1, "cat sat": 1, "sat on": 1, "on the": 1, "the mat": 1 o Bigram counts are all 1. Prepared by :Ayesha Arab Natural Language Processing Prepared by :Ayesha Arab Natural Language Processing Kneser-Ney Smoothing Concept: Kneser-Ney Smoothing builds on Good-Turing by incorporating context from lower- order models. It redistributes probability mass to unseen bigrams based on the likelihood of the preceding context. Steps: 1. Calculate Continuation Probability: o Calculate the probability of the bigram in the context of lower-order (unigram) models. Interpolation Smoothing Concept: Interpolation smoothing combines multiple levels of n-gram models (e.g., unigram, bigram) to estimate probabilities, allowing blending of different models. Prepared by :Ayesha Arab Natural Language Processing Summary Turing Smoothing adjusts for unseen bigrams by redistributing the probability of observed bigrams. Kneser-Ney Smoothing refines this by leveraging context from lower-order models. Interpolation Smoothing combines multiple models to estimate probabilities, blending unigram and bigram probabilities. Prepared by: Ayesha Arab Natural Language Processing CHP 3 TEXT REPRESENTATION What is Text Represenation in NLP. Explain in Detail. Text representation in Natural Language Processing (NLP) refers to the techniques used to convert text into a format that can be easily processed by machine learning algorithms and models. Since computers cannot directly understand human language, text representation transforms text into numerical or vector formats that models can work with. Here are some key methods of text representation: 1. Bag of Words (BoW) Definition: A text representation technique where each document is represented as a vector of word counts. How it Works: o Create a vocabulary of all unique words in the corpus. o Represent each document by counting the occurrences of each word in this vocabulary. Example: For documents "I love India" and "I love programming," the BoW representation might be: o Document 1: [1, 1, 1, 0] (where the vector corresponds to counts of ["I", "love", "India", "programming"]) o Document 2: [1, 1, 0, 1] 2. Term Frequency-Inverse Document Frequency (TF-IDF) Definition: A numerical statistic that reflects the importance of a word in a document relative to the corpus. How it Works: o Term Frequency (TF): Measures how often a word appears in a document. o Inverse Document Frequency (IDF): Measures how important a word is across the entire corpus. o TF-IDF: The product of TF and IDF, giving a weighted score for each word. Example: For the terms "love" in Document 1 and "programming" in Document 2, their TF-IDF scores will highlight the importance of each word in its respective document relative to the corpus. 3. Word Embeddings Prepared by: Ayesha Arab Natural Language Processing Definition: Vector representations of words where semantically similar words are mapped to nearby points in a continuous vector space. How it Works: o Use models like Word2Vec, GloVe, or FastText to learn word representations based on context and semantic meaning. Example: The words "king" and "queen" might have similar vectors, capturing their semantic relationship. 4. One-Hot Encoding Definition: A sparse vector representation where each word is represented by a vector with a single 1 and all other values as 0. How it Works: o Assign a unique index to each word in the vocabulary. o Represent each word as a vector with a 1 in the index corresponding to the word and 0 elsewhere. Example: In a vocabulary of ["cat", "dog", "fish"], "cat" might be represented as [1, 0, 0]. 5. Character N-Grams Definition: Representations based on contiguous sequences of characters. How it Works: o Extract n-grams (e.g., bigrams, trigrams) from the text and represent them as features. Example: For the word "cat", bigrams might be ["ca", "at"]. 6. Sentence Embeddings Definition: Representations of entire sentences or phrases as vectors. How it Works: o Use models like Universal Sentence Encoder or BERT to generate embeddings for sentences, capturing context and meaning. Example: The sentences "I love India" and "India is wonderful" would be mapped to vectors that capture their semantic similarity. Prepared by: Ayesha Arab Natural Language Processing Bag of Words (BoW) in Detail with example Theory: The Bag of Words model is a simple way to convert text data into numerical features that can be used by machine learning algorithms. In BoW, the text is represented as a "bag" of its words, ignoring grammar and word order but keeping track of the number of times each word occurs. Steps: 1. Create a Vocabulary: Identify all unique words in the corpus (collection of documents). 2. Count Frequencies: Count the number of occurrences of each word in the text. Example: Corpus: 1. Document 1: "The cat sat on the mat." 2. Document 2: "The cat is fat." Step-by-Step: 1. Create Vocabulary: o Unique Words: [The, cat, sat, on, mat, is, fat] 2. Count Frequencies: For Document 1: o The: 2 o cat: 1 o sat: 1 o on: 1 o mat: 1 o is: 0 o fat: 0 For Document 2: o The: 1 o cat: 1 o sat: 0 o on: 0 o mat: 0 Prepared by: Ayesha Arab Natural Language Processing o is: 1 o fat: 1 BoW Representation: Document 1: [2, 1, 1, 1, 1, 0, 0] Document 2: [1, 1, 0, 0, 0, 1, 1] Here, each document is represented as a vector of word counts. TF-IDF (Term Frequency-Inverse Document Frequency) Theory: TF-IDF is a statistical measure used to evaluate the importance of a word in a document relative to a collection of documents (corpus). It balances two factors: Term Frequency (TF): How frequently a term occurs in a document. Inverse Document Frequency (IDF): How important the term is in the entire corpus. Words that are common in many documents have lower importance. Example: Corpus: 1. Document 1: "The cat sat on the mat." 2. Document 2: "The cat is fat." Step-by-Step: 1. Calculate Term Frequency (TF): For Document 1: Prepared by: Ayesha Arab Natural Language Processing o TF(The) = 2/6 = 0.33 o TF(cat) = 1/6 = 0.17 o TF(sat) = 1/6 = 0.17 o TF(on) = 1/6 = 0.17 o TF(mat) = 1/6 = 0.17 o TF(is) = 0/6 = 0 o TF(fat) = 0/6 = 0 For Document 2: o TF(The) = 1/5 = 0.20 o TF(cat) = 1/5 = 0.20 o TF(sat) = 0/5 = 0 o TF(on) = 0/5 = 0 o TF(mat) = 0/5 = 0 o TF(is) = 1/5 = 0.20 o TF(fat) = 1/5 = 0.20 2. Calculate Inverse Document Frequency (IDF): 3. Calculate TF-IDF: For Document 1: TF-IDF(The) = 0.33 × 0 = 0 TF-IDF(cat) = 0.17 × 0 = 0 Prepared by: Ayesha Arab Natural Language Processing TF-IDF(sat) = 0.17 × 0.30 ≈ 0.051 TF-IDF(on) = 0.17 × 0.30 ≈ 0.051 TF-IDF(mat) = 0.17 × 0.30 ≈ 0.051 TF-IDF(is) = 0 × 0.30 = 0 TF-IDF(fat) = 0 × 0.30 = 0 For Document 2: TF-IDF(The) = 0.20 × 0 = 0 TF-IDF(cat) = 0.20 × 0 = 0 TF-IDF(sat) = 0 × 0.30 = 0 TF-IDF(on) = 0 × 0.30 = 0 TF-IDF(mat) = 0 × 0.30 = 0 TF-IDF(is) = 0.20 × 0.30 ≈ 0.060 TF-IDF(fat) = 0.20 × 0.30 ≈ 0.060 Summary Bag of Words (BoW): Represents text as a collection of word counts, ignoring word order but tracking frequency. TF-IDF: Adjusts word frequencies by considering their importance across the entire corpus, helping to highlight significant terms. Vector Space Model (VSM) Vector Space Model (VSM) is a way to represent text data (like documents or sentences) as vectors (numerical arrays) in a high-dimensional space. This model helps in analyzing and processing text by converting it into a format that can be easily handled by mathematical and computational methods. 1. Concept of Vector Space Model Imagine you have a huge library of documents. To analyze and compare these documents, we first need a way to represent each document in a format that can be compared numerically. The Vector Space Model does this by converting each document into a vector in a multi-dimensional space. Key Points: Dimensional Space: Each unique word in the corpus (the collection of documents) represents a dimension in this space. So, if there are 1,000 unique words, we have a 1,000- dimensional space. Prepared by: Ayesha Arab Natural Language Processing Vectors: Each document is represented as a vector in this space. The position and length of this vector depend on the frequency and importance of each word in the document. 2. How It Works Step 1: Build Vocabulary Vocabulary Creation: First, list all unique words from the entire corpus. This forms the basis of our dimensional space. Example: For documents with words "I love India" and "I love programming", the vocabulary is ["I", "love", "India", "programming"]. Step 2: Represent Documents as Vectors Vector Construction: Each document is converted into a vector. Each dimension in this vector corresponds to a word in the vocabulary. The value in each dimension represents the importance or frequency of that word in the document. o Example Document 1 ("I love India"): ▪ Vector: [1, 1, 1, 0] (Here, 1 means the word is present in the document, and 0 means it’s not.) o Example Document 2 ("I love programming"): ▪ Vector: [1, 1, 0, 1] Step 3: Compute Similarities Similarity Measures: Once documents are converted into vectors, you can compute similarities between them using methods like cosine similarity. This helps in tasks like document retrieval or clustering. o Cosine Similarity: Measures the angle between two vectors. The smaller the angle, the more similar the documents are. o Example Calculation: ▪ Cosine similarity between Document 1 and Document 2 vectors can be computed using the dot product and magnitudes of the vectors. 3. Applications Information Retrieval: Finding documents that are similar to a given query. Text Classification: Categorizing documents into predefined categories. Clustering: Grouping similar documents together. 4. Example Documents: Prepared by: Ayesha Arab Natural Language Processing 1. "I love India" 2. "I love programming" 3. "India is beautiful" Vocabulary: ["I", "love", "India", "programming", "is", "beautiful"] Vectors for Each Document: Document 1: [1, 1, 1, 0, 0, 0] Document 2: [1, 1, 0, 1, 0, 0] Document 3: [0, 0, 1, 0, 1, 1] Cosine Similarity Calculation: Compare Document 1 with Document 2 using their vectors to determine how similar they are. The formula for calculating cosine similarity between two vectors A and B is as follows: Summary The Vector Space Model represents text documents as vectors in a multi-dimensional space, where each dimension corresponds to a unique word from the corpus. This representation allows for numerical comparison and analysis of text, facilitating various NLP tasks like document retrieval, classification, and clustering. By transforming text into vectors, we can apply mathematical techniques to understand and work with textual data effectively. Latent Semantic Analysis(LSA) Latent Semantic Analysis (LSA) is a technique in Natural Language Processing (NLP) used to discover the underlying (latent) meaning of words and documents. It helps to uncover relationships between words that are not immediately obvious by examining large amounts of text data. Here’s a detailed breakdown of LSA in simple terms: 1. Concept of Latent Semantic Analysis Latent Semantic Analysis is like a detective that tries to find hidden meanings and relationships between words in a document or a set of documents. It helps to: Prepared by: Ayesha Arab Natural Language Processing Find similarities between words and documents. Identify topics or themes that are not directly apparent. 2. How LSA Works Step 1: Create a Document-Term Matrix Document-Term Matrix: Start by creating a matrix where rows represent documents and columns represent unique words (terms). Each cell in this matrix shows the frequency of a word in a document. Example: For documents: 1. "I love India" 2. "India is a beautiful country" 3. "Programming in Python is fun" Step 2: Apply Singular Value Decomposition (SVD) SVD: Decompose the document-term matrix into three smaller matrices: o U (Document-Topic Matrix): Shows the relationship between documents and topics. o Σ (Singular Values Matrix): Represents the strength of each topic. o V^T (Term-Topic Matrix): Shows the relationship between terms and topics. Example: o U might show that Document 1 is strongly related to topics about "India" and "Love." o Σ might show that the first topic (related to "India") is the most important. o V^T might show that "India" and "Beautiful" are strongly related to the topic about "India." Prepared by: Ayesha Arab Natural Language Processing Step 3: Reduce Dimensions Dimensionality Reduction: Reduce the number of topics (dimensions) to focus on the most significant ones. This helps in capturing the underlying meaning without the noise of less important details. Example: o Instead of considering all terms, focus on the key topics like "India" and "Programming." Step 4: Analyze Relationships Analyze Relationships: Use the reduced matrices to find similarities between documents or between terms. This helps in identifying topics or themes across the documents. Example: o You can find that "India" and "Beautiful" are related to a common topic about India, even if these words don’t appear together in the same document. 3. Applications of LSA Information Retrieval: Improve search results by understanding the context of search terms. Text Classification: Group similar documents or texts based on underlying themes. Topic Modeling: Identify the main topics in a collection of documents. SVD Application and Dimension Reduction: After applying SVD and reducing dimensions, you might find that "India" and "Beautiful" are related to a topic about "India," and "Programming" and "Python" are related to a different topic. Word2Vec Word2Vec is a popular word embedding technique developed by Google. It represents words as dense vectors in a continuous vector space, capturing semantic similarities between words. How Word2Vec Works 1. Models: o Continuous Bag of Words (CBOW): Predicts the current word based on a context of surrounding words. o Skip-gram: Predicts surrounding words based on the current word. 2. Training: Prepared by: Ayesha Arab Natural Language Processing o Input: A large corpus of text. o Output: Dense vectors for each word where similar words have similar vectors. Example Corpus: 1. "I love programming" 2. "I love coffee" 3. "Programming is fun" 4. "Coffee is great" Skip-gram Model: Current Word: "love" Context Words: ["I", "programming", "coffee"] Word Vectors (after training): "love": [0.2, 0.8, 0.1,..., 0.4] "programming": [0.3, 0.7, 0.2,..., 0.5] "coffee": [0.4, 0.6, 0.2,..., 0.3] Word Similarity: The vector for "love" might be close to the vectors for "programming" and "coffee" because they appear in similar contexts. GloVe (Global Vectors for Word Representation) GloVe is an embedding technique developed by Stanford University that represents words in a dense vector space based on word co-occurrence statistics from a corpus. How GloVe Works 1. Matrix Factorization: o Input: A word co-occurrence matrix (counts of how often words appear together). o Output: Dense vectors for each word. 2. Training: o GloVe factorizes the co-occurrence matrix to find vectors that capture word similarity based on global statistical information. Example Prepared by: Ayesha Arab Natural Language Processing Corpus: 1. "I love programming" 2. "I love coffee" 3. "Programming is fun" 4. "Coffee is great" Co-occurrence Matrix: Counts how often each word pair appears together. Word Vectors (after training): "love": [0.3, 0.7, 0.2,..., 0.5] "programming": [0.4, 0.6, 0.3,..., 0.4] "coffee": [0.5, 0.5, 0.4,..., 0.2] Word Similarity: "love" might be close to "programming" and "coffee" based on how often these words co- occur in the corpus. FastText FastText is an extension of Word2Vec developed by Facebook that represents words as bags of character n-grams, capturing morphological information. How FastText Works 1. Character N-grams: o Break words into character n-grams (subword units) and use these to generate embeddings. 2. Training: o Input: A corpus of text. o Output: Dense vectors for words that include information from character n-grams. Example Corpus: 1. "I love programming" 2. "I love coffee" 3. "Programming is fun" Prepared by: Ayesha Arab Natural Language Processing 4. "Coffee is great" Character N-grams for "programming": "prog", "rogram", "ogramm", "grammi", "rammin", "ammin", "mming",... Word Vectors (after training): "love": [0.2, 0.8, 0.1,..., 0.4] "programming": [0.3, 0.7, 0.2,..., 0.5] "coffee": [0.4, 0.6, 0.2,..., 0.3] Word Similarity: "love" is similar to "programming" and "coffee" based on both word and subword information. Doc2Vec Doc2Vec is an extension of Word2Vec that generates vector representations for entire documents or sentences, not just individual words. How Doc2Vec Works 1. Document Vectors: o Adds a unique vector for each document to capture context beyond individual words. 2. Training: o Input: A corpus of text where each document is tagged with a unique ID. o Output: Dense vectors for each document or sentence. Example Corpus: 1. "I love programming" 2. "I love coffee" 3. "Programming is fun" 4. "Coffee is great" Document Vectors: For document "I love programming": [0.3, 0.7, 0.2,..., 0.5] For document "I love coffee": [0.4, 0.6, 0.3,..., 0.4] Prepared by: Ayesha Arab Natural Language Processing Sentence Embedding: "I love programming" might be represented by a vector that captures the context and meaning of the entire sentence. Similarity: Comparing the vectors for different sentences can help identify their similarity based on their embeddings. Summary Word2Vec: Learns word vectors based on context using models like Skip-gram and CBOW. GloVe: Uses global word co-occurrence statistics to learn word vectors. FastText: Enhances Word2Vec by considering character n-grams. Doc2Vec: Extends Word2Vec to generate vectors for entire documents or sentences. Example Comparison: Word2Vec: "run" and "running" might have different vectors, as the model doesn't account for their morphological similarity. GloVe: "run" and "running" might be closer if they frequently co-occur with similar words like "athlete" or "marathon." FastText: "run" and "running" will be similar because the model breaks them into common subword parts like "run" and "ing." Prepared by Ayesha Arab NATURAL LANGUAGE PROCESSING(NLP) CHP 4: TEXT CLASSIFICATION & CLUSTERING Text Classification Problem: Introduction to Text Classification: Text classification is a fundamental task in Natural Language Processing (NLP) that involves categorizing text into predefined categories. This problem is highly relevant in various applications, such as spam detection, sentiment analysis, news categorization, and more. The main goal is to automatically assign labels to text based on its content. Understanding the Basics: Text classification works by analyzing the words, phrases, or even the entire content of the text to determine which category it belongs to. For instance, in email spam detection, the classifier decides whether an email is "spam" or "not spam." In sentiment analysis, it might classify a product review as "positive," "negative," or "neutral." Steps Involved in Text Classification: 1. Data Collection: o The first step is to collect the data that needs to be classified. This could be emails, social media posts, product reviews, news articles, etc. The data is typically labeled, meaning that each text has a corresponding category. 2. Preprocessing the Text: o Raw text data needs to be preprocessed before feeding it to the classifier. Preprocessing includes steps like: ▪ Tokenization: Splitting the text into individual words or tokens. ▪ Lowercasing: Converting all text to lowercase to maintain uniformity. ▪ Removing Stopwords: Removing common words like "the," "is," "in," etc., that don’t contribute much to the meaning. ▪ Stemming or Lemmatization: Reducing words to their root form (e.g., "running" to "run"). ▪ Vectorization: Converting text into numerical form using techniques like Bag of Words (BoW), TF-IDF (Term Frequency-Inverse Document Frequency), or word embeddings. 3. Choosing a Model: o After preprocessing, the text is ready to be classified. Various machine learning models can be used for text classification, such as: ▪ Naive Bayes: A simple probabilistic model based on Bayes' theorem, often used for text classification due to its efficiency. Prepared by Ayesha Arab NATURAL LANGUAGE PROCESSING(NLP) ▪ Support Vector Machines (SVM): A powerful model that tries to find the optimal boundary between different categories. ▪ Deep Learning Models: Models like Convolutional Neural Networks (CNN) or Recurrent Neural Networks (RNN) can also be used for more complex text classification tasks. 4. Training the Model: o The selected model is trained using the labeled dataset. The model learns to recognize patterns and associations between the text and its corresponding category. 5. Testing and Evaluation: o After training, the model is tested on a separate set of data to evaluate its performance. Common evaluation metrics include accuracy, precision, recall, and F1-score. 6. Deployment: o Once the model performs well, it can be deployed in a real-world application where it can classify new, unseen text. Challenges in Text Classification: Ambiguity: The same word or phrase can have different meanings depending on the context, making classification difficult. Large Vocabulary: The vast number of words and phrases in natural language increases the complexity. Data Imbalance: Sometimes, certain categories have more examples than others, leading to biased models. Feature Engineering: Deciding which features (words, phrases) to use for classification can be challenging. Applications of Text Classification: Spam Detection: Automatically filtering out spam emails. Sentiment Analysis: Determining the sentiment of reviews or social media posts. Topic Categorization: Classifying news articles or documents by topic (e.g., sports, politics, technology). Language Detection: Identifying the language in which a text is written. Prepared by Ayesha Arab NATURAL LANGUAGE PROCESSING(NLP) Feature Selection Introduction to Feature Selection: Feature selection is a process used in machine learning to select a subset of relevant features (variables, predictors) for model construction. The goal is to improve the model's performance by reducing the dimensionality of the data, thus minimizing overfitting, speeding up the training process, and enhancing the model's interpretability. Steps in Feature Selection: 1. Understanding the Data: o Begin by understanding the dataset, including the number of features and their types (categorical, numerical). Each feature may contribute differently to the outcome. 2. Importance of Feature Selection: o High-dimensional datasets can have many irrelevant or redundant features that do not contribute to the predictive power of the model. Removing these irrelevant features can lead to simpler, faster, and more accurate models. 3. Techniques for Feature Selection: o Filter Methods: These methods rank features based on statistical tests (like correlation, chi-square) before the model is trained. They are independent of the learning algorithm. ▪ Examples: Correlation matrix, Chi-Square test, Information Gain. o Wrapper Methods: These methods evaluate the model's performance with different subsets of features and select the one that produces the best result. ▪ Examples: Forward selection, Backward elimination, Recursive Feature Elimination (RFE). o Embedded Methods: These methods perform feature selection during the model training process. The learning algorithm itself decides which features contribute most to the accuracy. ▪ Examples: LASSO (Least Absolute Shrinkage and Selection Operator), Ridge Regression. 4. Evaluation of Selected Features: o After selecting features, the model is trained and evaluated on a validation set. The performance is compared to the model trained on the full feature set. Metrics like accuracy, precision, recall, and F1-score are used for evaluation. Prepared by Ayesha Arab NATURAL LANGUAGE PROCESSING(NLP) Challenges in Feature Selection: o Overfitting: Selecting too many features may lead to a model that performs well on training data but poorly on unseen data. o Computational Cost: Evaluating all possible subsets of features can be computationally expensive. o Domain Knowledge: Sometimes, domain expertise is required to decide which features are relevant. Applications of Feature Selection: Improved Model Performance: By reducing noise in the data, the model becomes more accurate. Faster Computation: Fewer features mean faster computation times, which is crucial for real-time applications. Enhanced Interpretability: With fewer features, it’s easier to understand the model's decisions. Conclusion: Feature selection is a critical step in the machine learning pipeline that enhances model performance and efficiency. By carefully choosing the most relevant features, we can build models that are not only accurate but also faster and easier to interpret. Naive Bayes Text Classification with Numerical Example Introduction to Naive Bayes: Naive Bayes is a probabilistic classifier based on Bayes' theorem. It’s particularly popular for text classification tasks because it’s simple, fast, and works well with large datasets. Prepared by Ayesha Arab NATURAL LANGUAGE PROCESSING(NLP) Bayes’ Theorem Recap: Numerical Example: Let’s consider a simple example where we want to classify a message as either "spam" or "not spam." Naive Bayes Classifier: A Numerical Example Problem: Classify a message as either "spam" or "not spam." Training Data: Class Message Spam Buy cheap pills Spam Cheap watches for sale Spam Get cheap loans Not Spam Meeting tomorrow Not Spam Lunch at 12 Prepared by Ayesha Arab NATURAL LANGUAGE PROCESSING(NLP) Not Spam Project deadline extended Step 1: Calculate Priors P(Spam) = 3/6 = 0.5 P(Not Spam) = 3/6 = 0.5 Step 2: Calculate Likelihood For "cheap pills for sale" given Spam: o P("cheap" | Spam) = 3/3 = 1.0 o P("pills" | Spam) = 1/3 = 0.33 o P("for" | Spam) = 2/3 = 0.67 o P("sale" | Spam) = 1/3 = 0.33 For "cheap pills for sale" given Not Spam: o P("cheap" | Not Spam) = 0/3 = 0.0 o P("pills" | Not Spam) = 0/3 = 0.0 o P("for" | Not Spam) = 0/3 = 0.0 o P("sale" | Not Spam) = 0/3 = 0.0 Step 3: Calculate Posterior For Spam: o P(Spam | "cheap pills for sale") = 1.0 * 0.33 * 0.67 * 0.33 * 0.5 = 0.0367 For Not Spam: o P(Not Spam | "cheap pills for sale") = 0.0 * 0.0 * 0.0 * 0.0 * 0.5 = 0.0 Conclusion: Since P(Spam | "cheap pills for sale") > P(Not Spam | "cheap pills for sale"), the message is classified as "Spam." k-Nearest Neighbors (k-NN) with Numerical Example Introduction to k-NN: k-NN is a simple, instance-based learning algorithm that classifies a data point based on the majority class among its nearest neighbors. Numerical Example: Suppose we have a dataset with two classes (A and B) and we want to classify a new point PPP. Training Data: Prepared by Ayesha Arab NATURAL LANGUAGE PROCESSING(NLP) Class A: (1, 1), (2, 2) Class B: (4, 4), (5, 5) New Point P=(3,3)P = (3, 3)P=(3,3) Step 1: Choose k: Let’s choose k=3k = 3k=3. Step 2: Calculate Distances: Distance from P to (1, 1): d = √((3-1)^2 + (3-1)^2) = √(2^2 + 2^2) = √(4 + 4) = √8 ≈ 2.83 Distance from P to (2, 2): d = √((3-2)^2 + (3-2)^2) = √(1^2 + 1^2) = √(1 + 1) = √2 ≈ 1.41 Distance from P to (4, 4): d = √((3-4)^2 + (3-4)^2) = √((-1)^2 + (-1)^2) = √(1 + 1) = √2 ≈ 1.41 Distance from P to (5, 5): d = √((3-5)^2 + (3-5)^2) = √((-2)^2 + (-2)^2) = √(4 + 4) = √8 Prepared by Ayesha Arab NATURAL LANGUAGE PROCESSING(NLP) ≈ 2.83 Step 3: Determine Nearest Neighbors: The 3 nearest neighbors are: (2, 2) from Class A (4, 4) from Class B (1, 1) from Class A Step 4: Majority Voting: Class A: 2 neighbors Class B: 1 neighbor Conclusion: The new point PPP is classified as Class A. FOR KNN , K -MEANS , SVM AND HIERARCHICHAL CLUSTERING YOU CAN REFER ML CHP 3-7 AND 9 Prepared by Ayesha Arab NATURAL LANGUAGE PROCESSING(NLP) Prepared by Ayesha Arab NATURAL LANGUAGE PROCESSING(NLP) Prepared by :Ayesha Arab NATURAL LANGUAGE PROCESSING(NLP) Morphology : Morphology is the branch of linguistics that studies the structure and form of words in a language, including the ways in which words are formed through the combination of morphemes (the smallest units of meaning). There are two main types of morphemes: 1. Free Morphemes: Can stand alone as words (e.g., "book", "run"). 2. Bound Morphemes: Cannot stand alone and must be attached to other morphemes (e.g., prefixes like "un-" in "undo", suffixes like "-ed" in "walked"). Morphological Analysis is crucial for natural language processing (NLP) tasks because it helps in understanding the meaning and function of words within sentences. For example, knowing that "cats" consists of the root "cat" and the plural suffix "-s" helps in determining the meaning of the word. Key Techniques used in Morphological Analysis for NLP Tasks o 1. Stemming o 2. Lemmatization o 3. Morphological Parsing o 4. Neural Network Models o 5. Rule-Based Methods o 6. Hidden Markov Models (HMMs) Importance of Morphological Analysis Morphological analysis is a critical step in NLP for several reasons: 1. Understanding Word Formation: It helps in identifying the basic building blocks of words, which is crucial for language comprehension. 2. Improving Text Analysis: By breaking down words into their roots and affixes, it enhances the accuracy of text analysis tasks like sentiment analysis and topic modeling. 3. Enhancing Language Models: Morphological analysis provides detailed insights into word formation, improving the performance of language models used in tasks like speech recognition and text generation. 4. Facilitating Multilingual Processing: It aids in handling the morphological diversity of different languages, making NLP systems more robust and versatile. Prepared by :Ayesha Arab NATURAL LANGUAGE PROCESSING(NLP) Part of Speech Tagging Part of Speech (POS) Tagging is the process of labeling each word in a sentence with its corresponding part of speech, such as noun, verb, adjective, etc. This is an essential step in many NLP tasks, including syntactic parsing and information retrieval. Example of POS Tagging Consider the sentence: “The quick brown fox jumps over the lazy dog.” After performing POS Tagging: “The” is tagged as determiner (DT) “quick” is tagged as adjective (JJ) “brown” is tagged as adjective (JJ) “fox” is tagged as noun (NN) “jumps” is tagged as verb (VBZ) “over” is tagged as preposition (IN) “the” is tagged as determiner (DT) “lazy” is tagged as adjective (JJ) “dog” is tagged as noun (NN) Workflow of POS Tagging: 1. Tokenization: Break down the text into smaller pieces called tokens, usually words or parts of words. This is the first step in NLP tasks. Prepared by :Ayesha Arab NATURAL LANGUAGE PROCESSING(NLP) 2. Loading Language Models: Load a language model using a library like NLTK or SpaCy. These models have been trained on large datasets and help understand the grammar of a language. 3. Text Processing: Clean the text by handling special characters, converting it to lowercase, or removing unnecessary information. This makes the text easier to analyze. 4. Linguistic Analysis: Analyze the text to identify the role of each word in the sentence, like whether it’s a noun, verb, adjective, etc. 5. Part-of-Speech Tagging: Assign a grammatical label (like noun, verb, etc.) to each word based on the analysis. 6. Results Analysis: Check the accuracy of the POS tags and make any necessary corrections. Types of POS Tagging: 1. Rule-Based Tagging How it Works: Uses predefined rules to assign POS tags to words. For example, a rule might tag any word ending in “-tion” as a noun. Example: In the sentence “The presentation highlighted the project’s development,” the rule-based tagger identifies “presentation” and “development” as nouns. Pros: Transparent and easy to understand since it doesn’t rely on machine learning. Cons: Limited by the rules it’s given and might not cover all language patterns. 2. Transformation-Based Tagging (TBT) How it Works: Starts with initial tags and then applies rules to modify them based on context. For example, it might change a word’s tag from a verb to a noun if it follows a determiner like “the.” Example: In “The cat chased the mouse,” TBT might incorrectly change “chased” to a noun because it follows “the.” Pros: More accurate than rule-based tagging, especially for complex grammar. Cons: Requires many rules and can be computationally expensive. 3. Statistical POS Tagging How it Works: Uses machine learning to predict the most likely POS tags based on patterns in large amounts of training data. Common models include Hidden Markov Models (HMMs). Example: In “The cat chased the mouse,” an HMM might correctly predict “chased” as a verb by considering the context. Prepared by :Ayesha Arab NATURAL LANGUAGE PROCESSING(NLP) Pros: Handles ambiguity and complex language patterns well. Cons: Depends on the quality and size of the training data. Advantages of POS Tagging Text Simplification: Makes complex sentences easier to understand. Information Retrieval: Improves search accuracy by tagging words based on their grammatical categories. Named Entity Recognition: Helps identify important entities like names and locations in text. Syntactic Parsing: Assists in analyzing sentence structure and word relationships. Disadvantages of POS Tagging Ambiguity: Words can have different meanings in different contexts, making tagging difficult. Idiomatic Expressions: Slang and idioms can confuse POS taggers because they don’t follow standard grammar. Out-of-Vocabulary Words: Words not in the training data might be mistagged. Domain Dependence: Taggers trained on one type of text might not work well on another without additional training data. Markov Model : A Markov Model is a mathematical model used to describe a system that transitions from one state to another, where the probability of each transition only depends on the current state and not on the sequence of events that preceded it. This is known as the Markov Property. In simple terms, the future state depends only on the present state and not on how the present state was reached. Components of a Markov Model 1. States: The different possible conditions or statuses that the system can be in. For example, in weather prediction, the states could be "Sunny," "Rainy," or "Cloudy." 2. Transition Probabilities: The probabilities of moving from one state to another. These probabilities are usually represented in a matrix form. Prepared by :Ayesha Arab NATURAL LANGUAGE PROCESSING(NLP) 3. Initial State: The state in which the system starts. Example of a Markov Model Let's consider a simple example: predicting the weather based on the current weather condition. States: - Sunny - Rainy - Cloudy Transition Probabilities: Suppose the weather changes from day to day according to the following probabilities: - If today is Sunny, the probability that tomorrow will be: - Sunny = 0.7 - Rainy = 0.2 - Cloudy = 0.1 - If today is Rainy, the probability that tomorrow will be: - Sunny = 0.3 - Rainy = 0.4 - Cloudy = 0.3 - If today is Cloudy, the probability that tomorrow will be: - Sunny = 0.4 - Rainy = 0.3 - Cloudy = 0.3 Transition Matrix: The transition probabilities can be represented in a matrix form as follows: Prepared by :Ayesha Arab NATURAL LANGUAGE PROCESSING(NLP) Predicting Future States Let’s say today is Sunny. We can use the transition probabilities to predict tomorrow’s weather. - Tomorrow's Weather: - Probability of Sunny = 0.7 - Probability of Rainy = 0.2 - Probability of Cloudy = 0.1 Thus, if today is sunny, there is a 70% chance that tomorrow will also be sunny, a 20% chance of rain, and a 10% chance of clouds. Multiple Steps Prediction If you want to predict the weather for the day after tomorrow, you use the current probabilities to calculate the next set of probabilities. For instance, if tomorrow turns out to be Rainy: - Day After Tomorrow’s Weather: - Probability of Sunny = 0.3 (if Rainy) 0.4 (Rainy → Sunny) + 0.7 (if Sunny) 0.7 (Sunny → Sunny) + 0.1 (if Cloudy) 0.4 (Cloudy → Sunny) = 0.21 + 0.49 + 0.04 = 0.74 - Probability of Rainy = 0.3 (Rainy → Rainy) + 0.2 (Sunny → Rainy) + 0.3 (Cloudy → Rainy) = 0.12 + 0.06 + 0.09 = 0.27 - Probability of Cloudy = 0.3 (Rainy → Cloudy) + 0.1 (Sunny → Cloudy) + 0.3 (Cloudy → Cloudy) = 0.09 + 0.07 + 0.09 = 0.25 This process can continue for as many steps as required. Prepared by :Ayesha Arab NATURAL LANGUAGE PROCESSING(NLP) Summary: A Markov Model is a simple yet powerful way to model a sequence of events where the probability of each event depends only on the state of the previous event. It's widely used in various fields, such as weather prediction, economics, and even text generation in Natural Language Processing. Hidden Markov Model(HMM): Hidden Markov Models (HMMs) are statistical models used in NLP for tasks like Part-of-Speech (POS) tagging, where the goal is to assign tags (e.g., noun, verb) to each word in a sentence. HMMs are particularly useful because they can model sequences of data, such as the sequence of words in a sentence. Key Components of HMM: 1. States: In the context of POS tagging, these are the POS tags (e.g., Noun, Verb, Adjective). The true sequence of states is hidden, hence the name "Hidden" Markov Model. 2. Observations: These are the actual words in the sentence. The model "emits" a word based on the state it’s in. 3. Transition Probabilities (A): The probability of moving from one state to another. For example, the probability of moving from a Noun to a Verb. 4. Emission Probabilities (B): The probability of a particular word being emitted given a state. For example, the probability of the word "dog" given the state Noun. 5. Initial Probabilities (π): The probability of starting in a particular state. How HMM Works in POS Tagging: - Goal: Given a sequence of words, find the most likely sequence of POS tags. - Approach: Use the HMM to calculate the most probable sequence of hidden states (POS tags) that could have produced the observed sequence of words. Example and Calculation: Suppose we want to tag the sentence: "He runs." 1. States: Noun (N), Verb (V) 2. Observations: "He", "runs" Prepared by :Ayesha Arab NATURAL LANGUAGE PROCESSING(NLP) Transition Probabilities (A): - P(Noun → Noun) = 0.3 - P(Noun → Verb) = 0.7 - P(Verb → Noun) = 0.4 - P(Verb → Verb) = 0.6 Emission Probabilities (B): - P(“He” | Noun) = 0.6 - P(“runs” | Noun) = 0.1 - P(“He” | Verb) = 0.1 - P(“runs” | Verb) = 0.7 Initial Probabilities (π): - P(Noun) = 0.5 - P(Verb) = 0.5 Step-by-Step Calculation: 1. Initialization: - For "He": P(Noun | "He") = P(Noun) P(“He” | Noun) = 0.5 0.6 = 0.3 P(Verb | "He") = P(Verb) P(“He” | Verb) = 0.5 0.1 = 0.05 2. Recursion (For "runs"): - P(Noun | "He runs") = max[(P(Noun | "He") P(Noun → Noun) P(“runs” | Noun)), (P(Verb | "He") P(Verb → Noun) P(“runs” | Noun))] = max[(0.3 0.3 0.1), (0.05 0.4 0.1)] = max[0.009, 0.002] = 0.009 Prepared by :Ayesha Arab NATURAL LANGUAGE PROCESSING(NLP) - P(Verb | "He runs") = max[(P(Noun | "He") P(Noun → Verb) P(“runs” | Verb)), (P(Verb | "He") P(Verb → Verb) P(“runs” | Verb))] = max[(0.3 0.7 0.7), (0.05 0.6 0.7)] = max[0.147, 0.021] = 0.147 3. Termination: The most probable sequence of states (POS tags) is "Noun Verb." Summary: In this example, the HMM has determined that the most likely sequence of POS tags for the sentence "He runs" is "Noun Verb." The model uses probabilities to make this decision, and the Viterbi algorithm is commonly used to efficiently find the most likely sequence of tags. Viterbi algorithm: The Viterbi algorithm is a method used to find the most likely sequence of states in a Hidden Markov Model (HMM). This algorithm is commonly used in Natural Language Processing (NLP), speech recognition, and bioinformatics, among other fields. Reference YT: https://www.youtube.com/watch?v=33SwqITvlBM Prepared by :Ayesha Arab NATURAL LANGUAGE PROCESSING(NLP) Maximum Entropy Models:(MaxEnt) Maximum Entropy Models are a type of machine learning model used in Natural Language Processing (NLP) and other fields. They help in making predictions by considering the most uniform (or "balanced") probability distribution that fits the data we have. Key Ideas: 1. Maximum Entropy Principle: The main idea is to choose the probability distribution that is as uniform as possible, without violating the known facts (constraints). This means the model makes the least amount of assumptions beyond what is provided by the data. 2. Features: In MaxEnt models, features are important pieces of information from the data. For example, in a text classification task, a feature might be whether a word appears in a sentence or not. 3. Learning the Model: The model learns from data by adjusting its parameters to maximize the probability of the observed outcomes, considering the features. It tries to find the most likely outcomes based on the features without making too many assumptions. 4. Predicting Outcomes: Once trained, the model can predict the likelihood of different outcomes for new data. For example, it might predict how likely a word is to be a noun or a verb in a sentence. How Maximum Entropy Models Work: 1. Define Features: Identify important characteristics from the data that might influence the outcome. For example, whether a word ends with "-ing" might be a feature in a part-of- speech tagging task. 2. Set Constraints: Use the training data to set up rules or constraints based on how often certain features occur with specific outcomes. 3. Optimization: The model adjusts its internal settings to find the most balanced probability distribution that fits the data. This step is often done using algorithms that iteratively improve the model. 4. Make Predictions: The trained model uses the probability distribution to predict the most likely outcome for new data. It doesn't just give a single answer but provides a range of possibilities with their likelihoods. Example: Part-of-Speech Tagging Imagine you want to figure out whether a word in a sentence is a noun, verb, or adjective. Features: The model might look at the word itself, the words around it, and how the word is used in other sentences. Prepared by :Ayesha Arab NATURAL LANGUAGE PROCESSING(NLP) Training: The model learns from a bunch of sentences where the correct part of speech for each word is already known. Prediction: For a new sentence, the model guesses the part of speech for each word based on what it has learned. Advantages of MaxEnt Models: 1. Flexibility: They can work with many types of features and handle complex relationships in the data. 2. Handling Overlaps: MaxEnt models can deal with situations where features overlap or are related to each other. 3. Probabilistic Output: Instead of just giving a single prediction, the model provides a probability for each possible outcome, showing how confident it is. Disadvantages: 1. Complexity: Training MaxEnt models can take a lot of time and computing power, especially with large datasets. 2. Need for Data: They require a good amount of training data to work effectively. Common Uses in NLP: 1. Part-of-Speech Tagging: Assigning the correct grammatical tags (like noun or verb) to words in a sentence. 2. Named Entity Recognition (NER): Identifying and categorizing entities like names, dates, and locations in text. 3. Text Classification: Categorizing pieces of text into different groups, like spam or not spam in emails. Simple Recurrent Neural Networks (RNNs) Simple Recurrent Neural Networks (RNNs) are a type of artificial neural network designed to handle sequences of data, like sentences or time series. They are called "recurrent" because they have loops that allow them to remember information from previous steps in a sequence. How Simple RNNs Work: 1. Sequential Data: RNNs are great for tasks where data comes in a sequence, such as words in a sentence or stock prices over time. 2. Memory: Unlike traditional neural networks that process each input independently, RNNs use their internal memory to keep track of what happened before. This memory is updated at each step as new data comes in. Prepared by :Ayesha Arab NATURAL LANGUAGE PROCESSING(NLP) 3. Loop Mechanism: At each step in the sequence, the RNN processes the current input and combines it with the information it remembers from previous steps. This helps it learn patterns over time. Example: Imagine you want to predict the next word in a sentence: "The cat sat on the ___". Input Sequence: "The", "cat", "sat", "on", "the". Memory: The RNN remembers the words it has seen so far, which helps it predict that "mat" is a likely word to complete the sentence. Applications of Recurrent Neural Networks (RNNs) RNNs are used in various applications where sequences or time-dependent data are important. Here are some common uses: 1. Speech Recognition: o Task: Converting spoken words into text. o How RNNs Help: They process the audio signal as a sequence of features, remembering past sounds to understand the current word better. 2. Language Translation: o Task: Translating text from one language to another. o How RNNs Help: They handle the sequence of words in the source language and generate the sequence of words in the target language. 3. Text Generation: o Task: Creating new text that mimics a given style or topic. o How RNNs Help: They learn patterns from existing text and generate new sequences that follow similar patterns. 4. Sentiment Analysis: o Task: Determining the sentiment (positive, negative, neutral) of a piece of text. o How RNNs Help: They understand the sequence of words and the context to analyze the overall sentiment of the text. 5. Time Series Prediction: o Task: Predicting future values based on past data (e.g., stock prices). o How RNNs Help: They use historical data to predict future values, remembering past trends and patterns. Prepared by :Ayesha Arab NATURAL LANGUAGE PROCESSING(NLP) In summary, Simple Recurrent Neural Networks (RNNs) are useful for tasks involving sequences because they can remember and use information from earlier in the sequence. They are widely applied in areas like speech recognition, language translation, text generation, sentiment analysis, and time series prediction. Recurrent Neural Network (RNN) vs LSTM vs Feed-Forward Neural Network Deep Neural Network vs Traditional Shallow Neural Network Prepared by :Ayesha Arab NATURAL LANGUAGE PROCESSING(NLP) Stacked Neural Networks Definition: Stacked Neural Networks involve layering multiple neural network layers on top of each other. The output from one layer becomes the input to the next, creating a "stack" of layers. Characteristics: Depth: The term "stacked" refers to the depth of the network. Stacking layers increases the model's capacity to learn complex representations. Hierarchical Feature Learning: Lower layers might learn simple features (e.g., edges in image processing), while higher layers learn more complex features (e.g., shapes, objects). Training: More layers can lead to improved performance on complex tasks, but may also require careful tuning to avoid issues like overfitting. Example: In image recognition, a stacked convolutional neural network (CNN) might include several convolutional layers followed by pooling layers and fully connected layers. Each layer captures different levels of abstraction. Applications: Image recognition Speech recognition Natural language processing Bidirectional Neural Networks Definition: Bidirectional Neural Networks process sequences in both forward and backward directions, allowing the model to capture information from past and future contexts simultaneously. 1. Inputting a Sequence: A BRNN takes a sequence of data points as input. Each data point is a vector (a list of numbers) with the same size. The sequence can vary in length. 2. Dual Processing: The BRNN processes the sequence in two directions: o Forward Direction: Processes the sequence from the beginning to the end. o Backward Direction: Processes the sequence from the end to the beginning. 3. Computing Hidden States: Prepared by :Ayesha Arab NATURAL LANGUAGE PROCESSING(NLP) Forward Pass: For each step in the sequence, the BRNN calculates a "hidden state" using the current input and the hidden state from the previous step. Backward Pass: It also calculates a hidden state in the reverse direction using the current input and the hidden state from the next step. 4. Determining the Output: The hidden states from both directions are used to determine the output. This output can be the final result or used as input for the next layer of the network. 5. Training: The BRNN is trained by comparing its output to the actual results. It adjusts its weights (the values that determine how inputs are transformed) to improve accuracy, using a method called backpropagation. Prepared by :Ayesha Arab NATURAL LANGUAGE PROCESSING(NLP) Comparison of RNN, LSTM, and GRU Models for Sequence Data Analysis 1. Neural Networks Overview: - Neural Networks consist of interconnected nodes organized into layers, with input, hidden, and output layers. - They are powerful in learning from data and adjusting internal parameters during training. 2. Forward and Backward Propagation: - Forward propagation involves data flow through the network and prediction generation. - Backward propagation includes refining internal parameters through techniques like gradient descent. 3. Importance of Sequences in NLP: - Sequences are vital in NLP tasks where order and context influence interpretation. - RNNs are specialized in processing sequential data like language understanding and generation. 4. Recurrent Neural Networks (RNN): - RNNs process data sequentially using recurrent connections and hidden states. - They excel in short to medium sequences but face challenges with long-term dependencies. 5. Long Short-Term Memory (LSTM): - LSTMs address long-term dependencies effectively with advanced memory handling. - They are complex but powerful in processing sequences with crucial long-term information. 6. Gated Recurrent Unit (GRU): - GRUs are simplified variations of RNNs, efficient in handling long-term dependencies. Prepared by :Ayesha Arab NATURAL LANGUAGE PROCESSING(NLP) - They use a reduced number of gates and combine cell state and hidden state for streamlined training. 7. RNN, LSTM, GRU - Comparison: - RNNs are foundational for sequence data analysis. - LSTMs excel in long-term dependency tasks, while GRUs offer efficiency in training. 8. Use Cases and Applications: - RNNs find applications in NLP, speech recognition, and time series prediction. - LSTMs are effective in handling complex sequences, while GRUs offer speed and efficiency in training. 9. GRUs as Efficient Alternatives to LSTMs: - GRUs offer a streamlined alternative to LSTMs - They handle sequential data with long-term dependencies while being computationally less complex 10. Comparison of RNN, LSTM, and GRU: - RNNs are ideal for short sequences but struggle with long-term dependencies - LSTMs excel in learning long-term dependencies despite higher complexity 11. Key Takeaways for Choosing Among RNN, LSTM, and GRU: - Choose RNNs for simplicity and short sequences - Opt for LSTMs for complex dependencies over extended periods 12. Performance and Efficiency: - GRUs are faster to train than LSTMs due to fewer parameters - The choice among RNN, LSTM, and GRU depends on specific task requirements Prepared by :Ayesha Arab NATURAL LANGUAGE PROCESSING(NLP) 13. Implementation of RNN, LSTM, and GRU Models: - Implemented RNN, LSTM, and GRU models with toy text data - Trained models for text generation using SimpleRNN, GRU, and LSTM 14. Concluding the NLP Journey: - Explored the depths of deep learning in NLP tasks - Preparing for Advanced Word Embedding Techniques in the next chapter Reference: Medium Article