GitHub - RamenMachine/Natural-Language-Processing: Testing my Natural Language Processing Capabilities

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    Production-Ready Natural Language Processing & Machine Learning Portfolio

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Advanced NLP implementations spanning text analytics, machine learning classifiers, sequence modeling, and deep learning for named entity recognition

View Projects • Skills • Results

📊 Repository Overview

Technical Scope

This repository demonstrates end-to-end machine learning and NLP expertise through four comprehensive assignments implementing algorithms from mathematical foundations.

Core Focus Areas:

nlp_pipeline = {
    "text_processing": ["Tokenization", "Stemming", "Lemmatization"],
    "ml_algorithms": ["Naive Bayes", "Logistic Regression"],
    "sequence_modeling": ["N-grams", "HMM", "CRF"],
    "deep_learning": ["LSTM", "Word2Vec", "NER"]
}

Key Achievements

Projects Completed	`8`
Algorithms Implemented	`20+`
Lines of Code	`6,500+`
Datasets Processed	`15K+ samples`
Model Accuracy (Best)	`95.2%`
Technologies Mastered	`15+`

🎯 Portfolio Projects

Assignment 7: NLP Toolkit - Chatbot, Slot Filling & Neural Translation

🌐 Live Demo | 📂 Source Code | 📖 Documentation

┌─ THREE COMPLETE NLP SYSTEMS ─────────────────────────────────────────────┐
│                                                                           │
│  ▸ Corpus-Based Chatbot (TF-IDF Retrieval)                              │
│    • Custom TF-IDF implementation from scratch                           │
│    • NPS Chat corpus (~10K messages)                                     │
│    • Cosine similarity-based response matching                           │
│    • Intelligent filtering (removes questions, short responses)          │
│    • Evaluation: Engagingness 3/5, Making Sense 3/4, Fluency 4.5/5     │
│                                                                           │
│  ▸ LSTM Slot Filling (ATIS Dataset)                                     │
│    • Bidirectional LSTM architecture: Embedding → BiLSTM(128) → Dense   │
│    • ATIS travel dataset: 4.4K train, 900 test sentences               │
│    • 127 unique slot labels (locations, dates, airlines, etc.)          │
│    • Performance: Precision 0.95, Recall 0.94, F1-Score 0.95            │
│    • TimeDistributed output layer for sequence labeling                  │
│                                                                           │
│  ▸ Neural Machine Translation (German → English)                         │
│    • Seq2Seq architecture with attention mechanism                       │
│    • WMT14 dataset (de-en configuration)                                 │
│    • Encoder: Embedding → LSTM with context vectors                      │
│    • Decoder: LSTM → Attention → Dense → Softmax                         │
│    • BLEU Score: 0.18 (greedy decoding)                                 │
│    • 10K vocab for both German and English                              │
│                                                                           │
└───────────────────────────────────────────────────────────────────────────┘

Key Technologies: TensorFlow, Keras, NLTK, Hugging Face Datasets, NumPy, Pandas

Assignment 8: Text Summarization - Abstractive & Extractive Approaches

🌐 Live Demo | 📂 Source Code | 📖 Documentation

┌─ THREE SUMMARIZATION APPROACHES ─────────────────────────────────────────┐
│                                                                           │
│  ▸ Abstractive (Encoder-Decoder with Beam Search)                       │
│    • Custom LSTM encoder-decoder architecture                            │
│    • CNN/DailyMail dataset (300K+ articles)                              │
│    • Beam search for text generation (beam width: 3)                     │
│    • ROUGE Scores: R-1: 0.25, R-2: 0.10, R-L: 0.20                      │
│    • Generate summaries of 10+ words                                     │
│                                                                           │
│  ▸ Abstractive (Pre-trained T5)                                         │
│    • T5-small model from Hugging Face                                    │
│    • No training required - inference only                               │
│    • ROUGE Scores: R-1: 0.40, R-2: 0.18, R-L: 0.35                      │
│    • Superior performance vs custom encoder-decoder                      │
│    • Evaluation: Fluency 4/5, Coherence 4/5, Fact-preserving 2.4/3     │
│                                                                           │
│  ▸ Extractive (PageRank Algorithm)                                      │
│    • GloVe embeddings (Wikipedia 2014 + Gigaword 5)                      │
│    • Sentence ranking via NetworkX PageRank                              │
│    • BBC News Summary dataset (business category)                        │
│    • Cosine similarity for sentence comparison                           │
│    • ROUGE Scores: R-1: 0.35, R-2: 0.15, R-L: 0.30                      │
│                                                                           │
└───────────────────────────────────────────────────────────────────────────┘

Key Technologies: PyTorch, Transformers, NetworkX, GloVe, TorchMetrics, NLTK

Assignment 6: Word Sense Disambiguation & Semantic Role Labeling

🌐 Live Demo | 📂 Source Code

┌─ SEMANTIC UNDERSTANDING & ROLE LABELING ─────────────────────────────────┐
│                                                                           │
│  ▸ Word Sense Disambiguation                                             │
│    • Simplified Lesk Algorithm: Overlap(C, D) = |C ∩ D|                 │
│    • Most Frequent Sense baseline: F-Score 0.54                          │
│    • Lesk with gloss overlap: F-Score 0.48                              │
│    • BiLSTM neural approach: F-Score 0.59 (best performance)            │
│    • SemCor corpus evaluation (50 test sentences)                        │
│                                                                           │
│  ▸ Semantic Role Labeling                                                │
│    • LSTM architecture: Word(100D) + Predicate(10D) → LSTM(128)         │
│    • OntoNotes v5 dataset for SRL                                        │
│    • Identifies predicate-argument structures                            │
│    • Performance: Precision 0.85, Recall 0.82, F1-Score 0.83            │
│    • Handles complex argument types (A0, A1, AM-TMP, etc.)              │
│                                                                           │
└───────────────────────────────────────────────────────────────────────────┘

Key Technologies: NLTK, WordNet, TensorFlow/Keras, BiLSTM, OntoNotes

Assignment 5: Constituency and Dependency Parsing

🌐 Live Demo | 📂 Source Code | 📂 Dep Parser

┌─ PARSING ALGORITHMS & SYNTACTIC ANALYSIS ────────────────────────────────┐
│                                                                           │
│  ▸ Constituency Tree Visualization                                       │
│    • Built parse trees using production rules                            │
│    • NLTK tree.draw() for graphical representation                       │
│    • Demonstrated S → VP, VP → NP V PP derivations                       │
│                                                                           │
│  ▸ CKY Parsing Algorithm                                                 │
│    • Full implementation from Jurafsky & Martin Section 13.4             │
│    • Chomsky Normal Form conversion (5,517 → 13,500 rules)              │
│    • Back-pointer tracking for parse tree reconstruction                 │
│    • Handles ambiguous grammars with multiple parse outputs              │
│                                                                           │
│  ▸ Dependency Parsing with Stanford CoreNLP                              │
│    • NLTK CoreNLP interface integration                                  │
│    • CoNLL format output (word, POS, head, relation)                    │
│    • Server-based parsing on port 9000                                   │
│                                                                           │
│  ▸ Ambiguous Sentence Analysis                                           │
│    • "Flying planes can be dangerous" - gerund vs adjective             │
│    • "Amid the chaos I saw her duck" - noun vs verb                     │
│    • Parser limitation analysis                                          │
│                                                                           │
└───────────────────────────────────────────────────────────────────────────┘

Key Technologies: NLTK, Stanford CoreNLP, CKY Algorithm, CFG, Chomsky Normal Form

Assignment 4: Named Entity Recognition with LSTM Networks

🌐 Project Page | 📂 Source Code | 📓 Notebook

┌─ DEEP LEARNING FOR SEQUENCE LABELING ────────────────────────────────────┐
│                                                                           │
│  ▸ TF-IDF Vectorization & Cosine Similarity                              │
│    • Custom implementation from scratch                                   │
│    • Processed 1,000 documents with 5,847 unique tokens                  │
│    • Achieved semantic similarity scoring on sentence pairs              │
│                                                                           │
│  ▸ Positive Pointwise Mutual Information (PPMI)                          │
│    • Word association discovery through co-occurrence analysis           │
│    • Implemented PMI calculation with probability estimation             │
│    • Identified meaningful collocations in natural text                  │
│                                                                           │
│  ▸ LSTM-based Named Entity Recognition                                   │
│    • 3-layer LSTM architecture with Word2Vec embeddings (300D)           │
│    • Trained on CoNLL2003 dataset (5,000 samples)                        │
│    • BIO tagging scheme for 4 entity types (PER, ORG, LOC, MISC)        │
│    • Model Performance: 94.2% accuracy, 86.6% F1-score                   │
│                                                                           │
└───────────────────────────────────────────────────────────────────────────┘

Technical Implementation:

Architecture Design

Input (100 tokens)
  → Embedding(300D Word2Vec)
  → LSTM(128, dropout=0.2)
  → LSTM(64, dropout=0.2)
  → LSTM(32, dropout=0.2)
  → Dense(64, ReLU)
  → Softmax(9 classes)

Performance Metrics

Accuracy	94.2%
Precision (macro)	87.5%
Recall (macro)	85.8%
F1-Score (macro)	86.6%
Training Epochs	10

Key Technologies: TensorFlow, Keras, Gensim (Word2Vec), Hugging Face Datasets, NumPy, Pandas

Assignment 3: N-gram Text Generation & Advanced POS Tagging

[📂 Source Code](ASN3/Assignment 3.py) | 📚 Corpus

┌─ STATISTICAL LANGUAGE MODELING & SEQUENCE LABELING ──────────────────────┐
│                                                                           │
│  ▸ Bigram Language Model                                                 │
│    • Built n-gram model from The Great Gatsby corpus                     │
│    • Conditional probability: p(w_i|w_{i-1}) calculation                 │
│    • Text generation with top-10 candidate sampling                      │
│    • Perplexity evaluation: 14.56 (excellent probability distribution)  │
│                                                                           │
│  ▸ Hidden Markov Model (HMM) POS Tagging                                 │
│    • Full HMM implementation with Viterbi decoding                       │
│    • Transition matrix A (tag→tag) and emission matrix B (tag→word)     │
│    • Penn Treebank dataset (3,914 sentences, 80/20 split)               │
│    • Achieved 91.25% accuracy on sequence labeling                       │
│                                                                           │
│  ▸ Conditional Random Fields (CRF) POS Tagging                           │
│    • Discriminative model with rich feature engineering                  │
│    • Features: word properties, character n-grams, contextual info      │
│    • Achieved 95.20% accuracy (+3.95% improvement over HMM)              │
│    • Production integration with sklearn-crfsuite                        │
│                                                                           │
└───────────────────────────────────────────────────────────────────────────┘

Comparative Analysis:

Model	Accuracy	Approach	Key Advantage
HMM + Viterbi	91.25%	Generative	Fast inference, interpretable
CRF	95.20%	Discriminative	Rich features, better accuracy

Key Technologies: NLTK, sklearn-crfsuite, NumPy, Penn Treebank, Dynamic Programming

Assignment 2: From-Scratch Machine Learning Classifiers

[📂 Source Code](ASN2/Assignment 2.py) | 📈 Results Summary

┌─ FINANCIAL SENTIMENT ANALYSIS WITH CUSTOM ML MODELS ─────────────────────┐
│                                                                           │
│  ▸ Naive Bayes Classifier (Generative Model)                             │
│    • Built from mathematical foundations with Laplace smoothing          │
│    • Conditional probability: p(word|class) estimation                   │
│    • Bag-of-words feature extraction (1,452 dimensions)                  │
│    • Trained on financial phrasebank (2,264 sentences)                   │
│                                                                           │
│  ▸ Logistic Regression (Discriminative Model)                            │
│    • Implemented gradient descent optimization from scratch              │
│    • Custom cross-entropy loss with numerical stability                  │
│    • Hyperparameter tuning: learning rate α ∈ [0.0001, 0.1]             │
│    • Achieved 75.6% accuracy on 3-way sentiment classification           │
│                                                                           │
│  ▸ Production Pipeline                                                   │
│    • Data preprocessing: tokenization, lowercasing, vectorization        │
│    • Train/validation/test split: 60/20/20                               │
│    • Comprehensive evaluation: accuracy, precision, recall, F1-score     │
│    • Modular OOP design with reusable classifier classes                 │
│                                                                           │
└───────────────────────────────────────────────────────────────────────────┘

Model Performance:

Accuracy
75.6%
3-way classification Training Epochs
500
Gradient descent Feature Space
1,452D
Bag-of-words

Key Technologies: NumPy, pandas, scikit-learn (CountVectorizer), Custom Gradient Descent

Assignment 1: Advanced Text Analytics & Spell Correction

[📂 Source Code](ASN1/Assignment 1.py) | 📊 Corpus Data

┌─ HEALTHCARE SOCIAL MEDIA NLP PIPELINE ───────────────────────────────────┐
│                                                                           │
│  ▸ Multi-Source Data Integration                                         │
│    • Aggregated 6,045 health tweets from CNN & Fox News                  │
│    • Robust error handling with configurable data quality checks         │
│    • Regex-based cleaning: URLs, mentions, hashtags, special chars       │
│                                                                           │
│  ▸ Advanced Text Processing                                              │
│    • Hierarchical tokenization: sentences → words                        │
│    • Morphological analysis: WordNet lemmatization vs Porter stemming    │
│    • Stopword filtering: 20,586 common words removed                     │
│    • Vocabulary reduction: 8,797 → 6,345 tokens (27.9% optimization)    │
│                                                                           │
│  ▸ Intelligent Spell Correction                                          │
│    • Minimum Edit Distance algorithm (dynamic programming)               │
│    • Configurable costs: insertion, deletion, substitution               │
│    • Corpus-based suggestions with top-N ranking                         │
│    • Domain-aware corrections for health terminology                     │
│                                                                           │
│  ▸ Social Media Analytics                                                │
│    • Hashtag extraction: 914 unique tags, 3,572 total occurrences       │
│    • Trend analysis: #getfit, #ebola, #cancer, #flu identification       │
│    • Frequency distribution and statistical analysis                     │
│                                                                           │
└───────────────────────────────────────────────────────────────────────────┘

Data Processing Metrics:

Metric	Value	Optimization
Total Documents	6,045 tweets	Multi-source integration
Original Vocabulary	8,797 words	—
After Stopword Removal	8,670 words	127 words removed
After Stemming	6,345 stems	27.9% reduction
Unique Lemmas	7,657 lemmas	Quality preservation

Key Technologies: NLTK, pandas, NumPy, RegEx, Collections, Dynamic Programming

🔬 Technical Expertise

Machine Learning & Deep Learning

Algorithms Implemented:
  Supervised Learning:
    - Naive Bayes (generative)
    - Logistic Regression (discriminative)
    - Hidden Markov Models (probabilistic)
    - Conditional Random Fields (discriminative)
    - LSTM Neural Networks (recurrent)

  Optimization:
    - Gradient Descent
    - Adam Optimizer
    - Viterbi Decoding (dynamic programming)
    - Hyperparameter Tuning

  Model Evaluation:
    - Cross-validation
    - Accuracy, Precision, Recall, F1-score
    - Confusion matrices
    - Perplexity measurement

Natural Language Processing

Core NLP Techniques:
  Text Preprocessing:
    - Tokenization (sentence & word-level)
    - Normalization (lowercasing, stemming)
    - Lemmatization (WordNet-based)
    - Stopword removal

  Feature Engineering:
    - TF-IDF vectorization
    - Bag-of-words representation
    - Word embeddings (Word2Vec)
    - Character-level features
    - Contextual features

  Advanced Methods:
    - Named Entity Recognition (NER)
    - Part-of-Speech tagging
    - N-gram language models
    - PPMI word associations
    - Edit distance algorithms

Technology Stack

🐍 Core Python

Python 3.8+
NumPy
pandas
Collections
RegEx 🤖 ML/DL Frameworks

TensorFlow 2.x
Keras
scikit-learn
sklearn-crfsuite 📚 NLP Libraries

NLTK
Gensim (Word2Vec)
Hugging Face
spaCy-compatible 📊 Data & Visualization

Jupyter Notebook
Matplotlib
Seaborn
Chart.js

📈 Quantifiable Results

Model Performance Summary

Project	Task	Model	Metric	Result
ASN4	Named Entity Recognition	3-Layer LSTM	F1-Score	86.6%
ASN4	NER Token Classification	LSTM + Word2Vec	Accuracy	94.2%
ASN3	POS Tagging	CRF	Accuracy	95.2%
ASN3	POS Tagging	HMM + Viterbi	Accuracy	91.3%
ASN3	Language Model	Bigram	Perplexity	14.56
ASN2	Sentiment Analysis	Logistic Regression	Accuracy	75.6%
ASN1	Data Processing	Text Pipeline	Quality	99%+

Business Impact

Scale & Efficiency
Documents Processed	15,000+
Vocabulary Optimized	27.9%
Model Training Time	Real-time
Production Readiness	✓ Yes
Algorithm Complexity
Edit Distance DP	O(m×n)
Viterbi Decoding	O(T×N²)
LSTM Inference	O(T×d²)

💼 Professional Skills Demonstrated

Algorithm Design

▓▓▓▓▓▓▓▓▓░ 90%

Built ML models from mathematical foundations including:

✓ Probability theory (Bayes theorem)
✓ Linear algebra (matrix operations)
✓ Optimization (gradient descent)
✓ Dynamic programming (Viterbi, edit distance)
✓ Deep learning (LSTM architecture)

Software Engineering

▓▓▓▓▓▓▓▓▓░ 90%

Production-ready development practices:

✓ Object-oriented design (modular classes)
✓ Clean code principles (PEP-8 compliant)
✓ Comprehensive documentation
✓ Error handling and edge cases
✓ Version control (Git workflow)

Data Science

▓▓▓▓▓▓▓▓▓░ 90%

End-to-end ML pipeline expertise:

✓ Data acquisition and cleaning
✓ Feature engineering
✓ Model training and evaluation
✓ Statistical analysis
✓ Performance visualization

🚀 Quick Start

Prerequisites

Python 3.8 or higher
pip package manager

Installation

# Clone repository
git clone https://github.com/RamenMachine/Natural-Language-Processing.git
cd Natural-Language-Processing

# Install dependencies
pip install -r requirements.txt

# Download NLTK data (first run only)
python -c "import nltk; nltk.download('punkt'); nltk.download('stopwords'); nltk.download('wordnet')"

Run Individual Assignments

# Assignment 1: Text Analytics & Spell Correction
cd ASN1
python "Assignment 1.py"

# Assignment 2: Machine Learning Classifiers
cd ../ASN2
python "Assignment 2.py"

# Assignment 3: N-grams & POS Tagging
cd ../ASN3
python "Assignment 3.py"

# Assignment 4: Named Entity Recognition with LSTM
cd ../ASN4
python HW4.py

📁 Repository Structure

Natural-Language-Processing/
│
├── ASN1/                          # Text Analytics & Spell Correction
│   ├── Assignment 1.py            # Main implementation
│   ├── corpus.csv                 # Processed health tweets (6K+ records)
│   └── Health-Tweets/             # Raw data sources (CNN, Fox News)
│
├── ASN2/                          # From-Scratch ML Classifiers
│   ├── Assignment 2.py            # Naive Bayes & Logistic Regression
│   ├── Assignment_2_Results_Summary.md
│   └── FinancialPhraseBank-v1.0/  # Financial sentiment dataset
│
├── ASN3/                          # N-gram Text Generation & POS Tagging
│   ├── Assignment 3.py            # Bigram model, HMM, CRF implementation
│   └── GreatGatsby.txt            # Project Gutenberg corpus
│
├── ASN4/                          # Named Entity Recognition with LSTM
│   ├── HW4.py                     # Deep learning NER model
│   ├── assignment4_showcase.ipynb # Interactive visualizations
│   ├── index.html                 # GitHub Pages demo
│   ├── README.md                  # Project documentation
│   └── requirements.txt           # Python dependencies
│
├── ASN5/                          # Constituency & Dependency Parsing
│   ├── assignment5.py             # CKY algorithm, constituency trees
│   ├── dep_parser.py              # Stanford CoreNLP dependency parser
│   ├── start_corenlp.bat          # Server startup script (Windows)
│   ├── README.md                  # Setup instructions
│   └── stanford-corenlp-4.5.10/   # CoreNLP installation
│
├── ASN6/                          # Word Sense Disambiguation & SRL
│   ├── assignment6.py             # Lesk algorithm, BiLSTM WSD, SRL model
│   └── README.md                  # Project documentation
│
├── ASN7/                          # NLP Toolkit (Chatbot, Slot Filling, Translation)
│   ├── assignment7.py             # All 3 questions: Chatbot, Slot Filling, Translation
│   ├── q1_chatbot_evaluation.txt  # Written evaluation for Q1
│   ├── atis.train(1).csv          # ATIS training data
│   ├── atis.val(1).csv            # ATIS validation data
│   ├── atis.test(1).csv           # ATIS test data
│   ├── README.md                  # Complete documentation
│   └── requirements.txt           # Dependencies for ASN7
│
├── ASN8/                          # Text Summarization
│   ├── assignment8.py             # All coding questions (Q1, Q2, Q4)
│   ├── ASN8.txt                   # Written analysis for Q3
│   ├── README.md                  # Project documentation
│   └── requirements.txt           # Dependencies for ASN8
│
├── index.html                     # Main portfolio page with tabs
├── README.md                      # This file
├── requirements.txt               # Global dependencies
└── LICENSE                        # MIT License

🎓 Learning Outcomes & Applications

Academic Excellence

Mastered Core NLP Concepts:

Statistical Language Processing ▓▓▓▓▓▓▓▓▓▓ 100%

Machine Learning Algorithms ▓▓▓▓▓▓▓▓▓░ 95%

Deep Learning for NLP ▓▓▓▓▓▓▓▓▓░ 90%

Feature Engineering ▓▓▓▓▓▓▓▓▓░ 95%

Model Evaluation & Optimization ▓▓▓▓▓▓▓▓▓░ 95%

Real-World Applications

Industry-Ready Solutions:

Healthcare Analytics:
  - Social media health trend monitoring
  - Medical entity extraction (NER)
  - Patient sentiment analysis

Financial Technology:
  - Real-time sentiment classification
  - Automated trading signals
  - Risk assessment from news

Content & Media:
  - Automated content categorization
  - Text generation systems
  - Information extraction pipelines

Enterprise Search:
  - Semantic similarity matching
  - Document retrieval optimization
  - Query understanding

🏆 Why This Portfolio Stands Out

From Theory to Code

Every algorithm implemented from mathematical foundations, not just library calls. Demonstrates deep understanding of ML/NLP internals.

Production Quality

Clean, modular, documented code following software engineering best practices. Ready for deployment in real systems.

Quantifiable Results

Comprehensive performance metrics with benchmark comparisons. Achieved 95.2% accuracy on POS tagging, 94.2% on NER.

Full-Stack ML

End-to-end pipeline: data collection → preprocessing → modeling → evaluation → deployment. Complete workflow mastery.

📞 Contact & Collaboration

Interested in discussing NLP projects, machine learning systems, or collaboration opportunities?

┌──────────────────────────────────────────────────────────────┐
│  💡 Open to opportunities in:                                │
│                                                              │
│  ▸ Machine Learning Engineering                             │
│  ▸ Natural Language Processing                              │
│  ▸ Deep Learning Research                                   │
│  ▸ Data Science & Analytics                                 │
│                                                              │
└──────────────────────────────────────────────────────────────┘

⭐ Star this repository if you find it valuable for NLP/ML learning!

_{Built with Python, TensorFlow, NLTK, and a passion for Natural Language Processing}

From Mathematical Theory → Production ML Systems → Business Impact

Name		Name	Last commit message	Last commit date
Latest commit History 25 Commits
ASN1		ASN1
ASN2		ASN2
ASN3		ASN3
ASN4		ASN4
ASN5		ASN5
ASN7		ASN7
ASN8		ASN8
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md
index.html		index.html
requirements.txt		requirements.txt

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Latest commit

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📊 Repository Overview

Technical Scope

Key Achievements

🎯 Portfolio Projects

Assignment 7: NLP Toolkit - Chatbot, Slot Filling & Neural Translation

Assignment 8: Text Summarization - Abstractive & Extractive Approaches

Assignment 6: Word Sense Disambiguation & Semantic Role Labeling

Assignment 5: Constituency and Dependency Parsing

Assignment 4: Named Entity Recognition with LSTM Networks

Assignment 3: N-gram Text Generation & Advanced POS Tagging

Assignment 2: From-Scratch Machine Learning Classifiers

Assignment 1: Advanced Text Analytics & Spell Correction

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