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Deeptapod Design and Implementation

1. Basic Text Processing

  • Tokenization
    • Word Tokenization
    • Sentence Tokenization
    • Subword Tokenization
    • Character Tokenization
  • Text Normalization
    • Case Conversion (Uppercase, Lowercase)
    • Accent and Diacritic Removal
    • Unicode Normalization
    • Stemming
    • Lemmatization
    • Spell Checking and Correction
  • Text Cleaning
    • Stopword Removal
    • Punctuation Removal
    • Number Removal
    • HTML/XML Tag Stripping
    • Text Deduplication
    • Emoticon and Emoji Removal
  • Substitution and Replacement
    • Synonym Replacement
    • Contraction Expansion
    • Slang Normalization
    • Abbreviation Expansion
    • Profanity Filtering
    • Named Entity Substitution

2. Advanced Text Processing

  • Named Entity Recognition (NER)
    • Entity Identification (e.g., Person, Organization, Location)
    • Fine-Grained Entity Recognition (e.g., Product Names, Medical Terms)
  • Part-of-Speech (POS) Tagging
    • Word-Level POS Tagging
    • Morphological Analysis
  • Dependency Parsing
    • Syntactic Dependency Parsing
    • Constituency Parsing
    • Shallow Parsing (Chunking)
  • Semantic Role Labeling (SRL)
    • Predicate-Argument Structure Identification
    • FrameNet-Based Labeling
  • Coreference Resolution
    • Pronoun Resolution
    • Anaphora and Cataphora Resolution
    • Cross-Document Coreference

3. Text Classification

  • Sentiment Analysis
    • Binary Sentiment Classification (Positive/Negative)
    • Multi-Class Sentiment Classification (e.g., Very Positive to Very Negative)
    • Aspect-Based Sentiment Analysis
  • Topic Modeling and Classification
    • Latent Dirichlet Allocation (LDA)
    • Non-Negative Matrix Factorization (NMF)
    • Correlation Topic Models
    • Supervised Topic Classification
  • Spam Detection and Filtering
    • Email Spam Detection
    • SMS Spam Detection
    • Web Content Filtering
  • Language Identification
    • Language Detection in Short Text
    • Language Identification in Multilingual Documents
  • Emotion Detection
    • Classification of Emotions (e.g., Joy, Anger, Sadness)
    • Detection of Mixed Emotions

4. Text Extraction

  • Keyword Extraction
    • TF-IDF Based Extraction
    • RAKE (Rapid Automatic Keyword Extraction)
    • Keyphrase Extraction
  • Text Summarization
    • Extractive Summarization
    • Abstractive Summarization
    • Multi-Document Summarization
    • Headline Generation
  • Information Retrieval
    • Document Retrieval
    • Passage Retrieval
    • Fuzzy Search
    • Boolean Search
  • Entity Extraction
    • Regular Expression-Based Extraction
    • Template-Based Extraction
    • Open Information Extraction (OpenIE)
  • Relation Extraction
    • Identification of Relationships between Entities
    • Triplet Extraction (Subject-Predicate-Object)
    • Temporal Relation Extraction
  • Event Extraction
    • Event Detection and Classification
    • Event Argument Extraction
    • Temporal Event Sequencing

5. Text Transformation

  • Machine Translation
    • Neural Machine Translation (NMT)
    • Statistical Machine Translation (SMT)
    • Phrase-Based Translation
    • Multilingual Translation
  • Text Generation
    • Language Modeling (e.g., GPT, BERT)
    • Story and Narrative Generation
    • Dialogue Generation (Chatbots)
    • Automatic Poetry Generation
    • Code Generation
  • Text Simplification
    • Lexical Simplification
    • Syntactic Simplification
    • Readability Improvement
  • Paraphrase Generation
    • Lexical Paraphrasing
    • Syntactic Paraphrasing
    • Sentence Compression
  • Text Style Transfer
    • Formal to Informal Conversion
    • Sentiment-Based Style Transfer
    • Author Imitation
    • Poetic Style Transfer

6. Text Analysis

  • Co-occurrence Analysis
    • Word Co-occurrence Matrix
    • Term Frequency-Inverse Document Frequency (TF-IDF)
    • Mutual Information
  • Sentiment Analysis
    • Aspect-Based Sentiment Analysis
    • Emotion Detection
    • Opinion Mining
  • Word Frequency Analysis
    • Frequency Distribution Analysis
    • N-Gram Frequency Analysis
  • Collocation Detection
    • Bigram and Trigram Collocation Detection
    • Statistical Measures (e.g., Pointwise Mutual Information)
  • Topic Coherence Analysis
    • Coherence Score Calculation
    • Topic Model Evaluation

7. Text Matching

  • Text Similarity
    • Cosine Similarity
    • Jaccard Similarity
    • Levenshtein Distance
    • BLEU Score (for translation)
    • ROUGE Score (for summarization)
    • Fuzzy Matching
  • Text Alignment
    • Sentence and Paragraph Alignment in Parallel Corpora
    • Document Alignment across Languages
  • Duplicate Detection
    • Near-Duplicate Detection
    • Document Plagiarism Detection

8. Text Enrichment

  • Contextual Embeddings
    • Word2Vec
    • GloVe
    • BERT, GPT, ELMo
    • Sentence Embeddings (e.g., Sentence-BERT)
  • Annotation
    • Manual Annotation (e.g., POS tagging, NER)
    • Crowdsourced Annotation
    • Automated Annotation Tools
  • Disambiguation
    • Word Sense Disambiguation
    • Entity Disambiguation
  • Text Normalization
    • Handling Noisy Text (e.g., Social Media, User-Generated Content)
    • Spelling Correction
    • Noise Removal (e.g., OCR Errors)

9. Text Segmentation

  • Sentence Boundary Detection
    • Rule-Based Sentence Segmentation
    • Machine Learning-Based Sentence Segmentation
  • Paragraph Segmentation
    • Text Structure Analysis
    • Thematic Segmentation
  • Topic Segmentation
    • TextTiling
    • Latent Semantic Analysis (LSA) for Segmentation
  • Discourse Analysis
    • Discourse Parsing
    • Rhetorical Structure Theory (RST) Analysis

10. Text Data Augmentation

  • Synthetic Data Generation
    • Data Augmentation for NLP Models
    • Synthetic Text Generation Using GANs
  • Back-Translation
    • Data Augmentation via Translation
  • Noise Injection
    • Random Noise Addition
    • Swap and Drop Techniques
  • Text Mixing
    • Interleaving Text from Multiple Sources
    • Generating Variants of Text Data

11. Text Visualization

  • Word Clouds
    • Frequency-Based Word Clouds
    • Topic-Based Word Clouds
  • N-gram Analysis
    • N-gram Frequency Visualization
    • N-gram Network Graphs
  • Topic Maps
    • Visualization of Topic Distributions
    • Topic Evolution Over Time
  • Dependency Trees
    • Visualization of Dependency Parse Trees
    • Syntactic Tree Visualization
  • Embedding Space Visualization
    • t-SNE or PCA Visualization of Word Embeddings
    • Clustering and Visualization of Sentence Embeddings

12. Text-based Learning and Prediction

  • Text Classification Models
    • Logistic Regression, SVM for Text Classification
    • Neural Network-Based Text Classifiers (e.g., CNNs, RNNs, Transformers)
  • Sequence Labeling
    • Named Entity Recognition (NER)
    • Part-of-Speech Tagging
    • Chunking and Shallow Parsing
  • Text Regression
    • Predicting Numerical Values from Text
    • Sentiment Score Prediction
  • Text Clustering
    • K-Means Clustering
    • Hierarchical Clustering
    • Topic-Based Clustering

13. Text Encryption and Obfuscation

  • Text Encryption
    • Symmetric and Asymmetric Encryption of Text
    • Hashing Techniques for Text Security (e.g., SHA, MD5)
  • Text Obfuscation
    • Obfuscating Text for Privacy (e.g., Pseudonymization)
    • Code Obfuscation (e.g., Obfuscating Source Code)
  • Steganography
    • Hiding Text within Images or Other Media
    • Watermarking Text Documents

14. Text Compression

  • Lossless Text Compression
    • Huffman Coding
    • Lempel-Ziv-Welch (LZW) Compression
    • Burrows-Wheeler Transform (BWT)
  • Lossy Text Compression
    • Text Summarization as Compression
    • Pruning and Filtering for Space Reduction
  • Language Modeling for Compression
    • Statistical Language Models for Efficient Encoding

15. Text Retrieval and Search

  • Information Retrieval (IR)
    • Boolean and Vector Space Models
    • BM25, TF-IDF Retrieval Models
    • Probabilistic Retrieval Models
  • Question Answering Systems
    • Open-Domain Question Answering
    • Knowledge-Based Question Answering
  • Conversational Agents
    • Chatbot Implementation
    • Dialogue Management and Response Generation

16. Speech-to-Text and Text-to-Speech

  • Automatic Speech Recognition (ASR)
    • Speech-to-Text Transcription
    • Speaker Diarization
  • Text-to-Speech (TTS)
    • Synthesis of Speech from Text
    • Neural TTS Models (e.g., Tacotron, WaveNet)
  • Voice-Based Interaction
    • Voice Command Recognition
    • Natural Language Understanding (NLU) for Voice Inputs

17. Cross-Linguistic Text Processing

  • Cross-Lingual Embeddings
    • Alignment of Embeddings Across Languages
    • Zero-Shot Learning in Multilingual Contexts
  • Bilingual Lexicon Induction
    • Extracting Bilingual Lexicons from Parallel Corpora
  • Cross-Lingual Information Retrieval
    • Retrieval Across Multiple Languages
  • Machine Translation
    • Low-Resource Language Translation
    • Domain-Specific Translation

18. Knowledge Representation and Reasoning

  • Knowledge Graph Construction
    • Extraction of Entities and Relations for Graph Construction
    • Ontology-Based Representation
  • Reasoning over Text
    • Deductive Reasoning
    • Abductive Reasoning
    • Commonsense Reasoning

19. Ethics and Bias in NLP

  • Bias Detection and Mitigation
    • Gender and Racial Bias Detection in Models
    • Mitigating Bias in Training Data and Models
  • Fairness in NLP
    • Ensuring Fairness Across Demographics
  • Privacy-Preserving NLP
    • Differential Privacy in Text Data
    • Secure Multi-Party Computation

20. Tools and Frameworks for NLP

  • Text Processing Libraries
    • NLTK, SpaCy, TextBlob
  • Deep Learning Frameworks
    • TensorFlow, PyTorch, Hugging Face Transformers
  • NLP Pipelines
    • AllenNLP, Stanford NLP
  • Pre-trained Models and Datasets
    • BERT, GPT, T5
    • Common Crawl, Wikipedia Dumps, OpenSubtitles