This handbook provides a comprehensive understanding of computational linguistics, focusing on the integration of deep learning in natural language processing (NLP). 18 edited chapters cover the state-of-the-art theoretical and experimental research on NLP, offering insights into advanced models and recent applications.
Highlights:
- Foundations of NLP: Provides an in-depth study of natural language processing, including basics, challenges, and applications.
- Advanced NLP Techniques: Explores recent advancements in text summarization, machine translation, and deep learning applications in NLP.
- Practical Applications: Demonstrates use cases on text identification from hazy images, speech-to-sign language translation, and word sense disambiguation using deep learning.
- Future Directions: Includes discussions on the future of NLP, including transfer learning, beyond syntax and semantics, and emerging challenges.
Key features:
- Comprehensive coverage of NLP and deep learning integration.
- Practical insights into real-world applications
- Detailed exploration of recent research and advancements through 16 easy to read chapters
- References and notes on experimental methods used for advanced readers
- Ideal for researchers, students, and professionals, this book offers a thorough understanding of computational linguistics by equipping readers with the knowledge to understand how computational techniques are applied to understand text, language and speech.
Readership:
- Researchers, students, and professionals in computer science and related fields (AI, ML, NLP and computational linguistics).
Table of Contents
- PREFACE
- KEY FEATURES
- LIST OF CONTRIBUTORS
- Rohit Vashisht, Sonia Deshmukh, Ambrish Gangal and Garima Singh
- 1. INTRODUCTION
- 2. EMERGENCY OF NLP
- 3. WORKING MODEL OF NLP
- 4. MAJOR APPLICATION OF NLP
- 5. NLP’s PRIME CHALLENGES
- CONCLUSION AND FUTURE DIRECTIONS
- REFERENCES
- LANGUAGE PROCESSING
- Asha Rani Mishra and Payal Garg
- 1. INTRODUCTION
- 1.1. Evolution in NLP
- 1.2. Recent Advancement in NLP
- 1.3. Applications in NLP
- 1.4. Role of Natural Language Processing in Text Mining
- 1.5. Challenges in Handling Text Data
- 2. REVIEW OF NATURAL LANGUAGE PROCESSING CONCEPTS, TECHNIQUES,
- TRENDS, AND APPLICATIONS
- 3. NATURAL LANGUAGE PROCESSING (NLP) IN TEXT SUMMARIZATION
- 3.1. Nature of Text Summarization According to Input
- 3.2. Nature of Text Summarization According to Output
- 3.3. Challenges in Text Summarization Approaches
- 4. GENERATING SUMMARY USING EXTRACTIVE APPROACH
- 5. PROPOSED METHODOLOGY
- 5.1. Steps in Textrank Algorithm
- 6. RESULTS AND DISCUSSION
- 6.1. Spacy
- 6.2. NLTK
- 6.3. Sumy
- 6.4. ROUGE Scores as Evaluation Metrics for Generated Summary
- CONCLUSION AND FUTURE SCOPE
- REFERENCES
- OVERVIEW
- Shahina Anjum and Sunil Kumar Yadav
- 1. INTRODUCTION
- 1.1. Categorization of NLP
- 1.2. Natural Language Processing Phases
- 2. REVIEW OF NATURAL LANGUAGE PROCESSING
- 3. NATURAL LANGUAGE TECHNIQUES
- 3.1. Popular NLP Techniques
- 3.1.1. Support Vector Machines
- 3.1.2. Neural Networks
- 3.1.3. Deep Learning Models
- 3.2. Traditional NLP Techniques
- 3.2.1. Probabilistic Models
- 3.2.2. N-Gram Models
- 3.2.3. Hidden Markov Models
- 3.3. Advanced NLP Techniques
- 3.3.1. Transfer learning
- a. Advantages of Performing Transfer Learning
- 3.3.2. Domain Adaptation
- 4. CATEGORIZATION OF NLP TECHNIQUES
- 5. ROLE OF NATURAL LANGUAGE PROCESSING IN LARGE PROJECTS
- 5.1. Challenges in NLP Learning Techniques
- CONCLUSION
- REFERENCES
- CLUSTERING APPLICATIONS
- Subhajit Ghosh
- 1. INTRODUCTION
- 2. REVIEW OF NLP CHALLENGES
- 3. NLP APPROACHES
- 4. TEXT CLUSTERING: AN ESSENTIAL TASK IN NLP
- 4.1. Challenges of Text Clustering
- 5. COMPUTATIONAL METHODOLOGY FOR TEXT CLUSTERING
- 5.1. Vector Space Model
- 5.2. Experiments with K-Means
- 5.3. Using GA
- 6. MACHINE TRANSLATION AND OTHER NLP APPLICATIONS
- CONCLUSION
- REFERENCES
- LEARNING AND ENSEMBLE METHOD
- Richa Singh, Rekha Kashyap and Nidhi Srivastava
- 1. INTRODUCTION
- 2. REVIEW OF FEDERATED LEARNING
- 3. RECENT RESEARCH IN NLP USING DEEP LEARNING
- 4. PROBLEM IDENTIFICATION
- 5. PROPOSED SOLUTION
- 6. RESULT
- 7. DISCUSSION
- CONCLUSION
- REFERENCES
- Rashmi Kumari, Subhranil Das, Raghwendra Kishore Singh and Abhishek Thakur
- 1. INTRODUCTION
- 2. NLP COMPONENTS
- 2.1. NLU
- 2.2. NLG
- 3. DEEP LEARNING FOR TEXT REPRESENTATION
- 3.1. Word Embeddings
- 3.2. Sentence and Document Embeddings
- 4. DEEP LEARNING FOR TEXT CLASSIFICATION (TC)
- 5. DEEP LEARNING FOR SEQUENCE LABELLING
- 5.1. POS
- 5.2. NER
- 5.3. Chunking and Parsing
- 6. DEEP LEARNING FOR TEXT GENERATION
- 7. APPLICATIONS OF DEEP LEARNING IN NLP
- CONCLUSION
- REFERENCES
- A SELF-COLLECTED DATASET APPROACH
- Sandeep Kumar Vishwakarma, Anuradha Pillai and Deepika Punj
- 1. INTRODUCTION
- 2. LITERATURE SURVEY
- 2.1. Image Dehazing Methods
- 2.2. Text Detection Methods
- 3. METHODOLOGY AND FRAMEWORK
- 3.1. Algorithm
- 4. EXPERIMENTAL SETUP AND RESULTS
- 4.1. Evaluation Results
- 4.2. Comparison with Existing Methods
- CONCLUSION
- REFERENCES
- LANGUAGE USING HINDI WORDNET DATASET
- Preeti Yadav, Sandeep Vishwakarma and Sunil Kumar
- 1. INTRODUCTION
- 2. HINDI WORDNET
- 2.1. The Application Programming Interface for the Hindi Wordnet
- 3. LITERATURE REVIEW
- 4. PROPOSED APPROACH AND FRAMEWORK
- 5. EXPERIMENTAL SETUP AND RESULTS
- CONCLUSION
- REFERENCES
- APPROACHES
- Shree Harsh Attri and Tarun Kumar
- 1. INTRODUCTION
- 2. APPROACHES TO BILINGUAL MACHINE TRANSLATION
- 2.1. Rule-based Techniques
- 2.2. Interlingua Approach
- 2.3. Example-based Techniques
- 2.4. Statistical Techniques
- 2.5. Rule-Based MT vs. Statistical MT
- 2.6. Soft Computing based Techniques
- 2.7. Neural Networks-based Techniques
- 2.8. Fuzzy Theory-based Techniques
- 2.9. Genetic Algorithms-based Techniques
- 2.10. Hybrid Techniques
- 3. PoS TAGGING IN MACHINE TRANSLATION
- 4. BILINGUAL PRE-PROCESSING TECHNIQUES
- 5. BILINGUAL MORPHOLOGICAL ANALYSES
- 6. BILINGUAL REVERSE MORPHOLOGICAL ANALYSIS
- 7. BILINGUAL POST-PROCESSING (PP) TECHNIQUES
- CONCLUSION
- REFERENCES
- SEQ2SEQ MODEL UTILIZING ATTENTION MECHANISM
- Sunil Kumar, Sandeep Kumar Vishwakarma, Abhishek Singh, Rohit Tanwar and
- Digvijay Pandey
- 1. INTRODUCTION
- 2. LITERATURE REVIEW
- 3. METHODOLOGY
- 3.1. Dataset
- 3.2. Data Preprocessing
- 3.3. Seq2Seq Model
- 3.4. LSTM
- 3.4.1. Uni-LSTM
- 3.4.2. Bi-LSTM
- 4. PROPOSED METHOD
- 4.1. LSTM Seq2Seq Model
- 4.2. Attention Mechanism
- 5. MATERIAL AND EXPERIMENTAL SETUP
- 5.1. Dataset and Source
- 5.2. Data Splitting
- 5.3. Data Preprocessing
- 5.4. Training
- 5.5. Evaluation
- 6. RESULT AND DISCUSSION
- CONCLUSION
- REFERENCES
- CURRENT DEVELOPMENTS, AND FUTURE PROSPECTS
- Neha Saini, Neha and Manjeet Singh
- 1. INTRODUCTION
- 2. LEVELS OF NLP
- 3. NATURAL LANGUAGE GENERATION
- 4. HISTORY OF NLP
- 5. RELATED WORK
- 6. APPLICATIONS OF NLP
- 7. RECENT TRENDS IN NLP
- 8. FUTURE OF NLP
- 8.1. Advanced Language Understanding
- 8.2. Multilingual and Cross-lingual NLP
- 8.3. Better Contextual Understanding
- 8.4. Few-shot and Zero-shot Learning
- 8.5. Ethical and Responsible AI
