The Kluwer international series in engineering and computer science Natural language processing and machine translation SECS 41 1st Edition by Robert Frederking ISBN 0898382556

The Kluwer international series in engineering and computer science Natural language processing and machine translation SECS 41 1st Edition by Robert Frederking – Ebook PDF Instant Download/Delivery. 0898382556
Full download The Kluwer international series in engineering and computer science Natural language processing and machine translation SECS 41 1st Edition after payment

Product details:
ISBN 10: 0898382556
ISBN 13:
Author: Robert Frederking

delves into the computational aspects of natural language processing (NLP) and its applications in machine translation. The book provides an in-depth exploration of algorithms, models, and techniques used to process and translate human languages through machines, with a focus on both theoretical foundations and practical implementations. Frederking covers essential topics such as syntactic parsing, statistical models, semantic analysis, and evaluation methods, while examining how these techniques can be applied to the complex task of machine translation. The book serves as both a comprehensive introduction and a reference for students, researchers, and professionals in computational linguistics, artificial intelligence, and related fields.

The Kluwer international series in engineering and computer science Natural language processing and machine translation SECS 41 1st Edition Table of contents:

Part I: Foundations of Natural Language Processing

2. Linguistic Fundamentals for NLP

   • Syntax and Grammar in Language
   • Morphology and Word Formation
   • Semantics and Meaning Representation
   • Pragmatics and Contextual Language Understanding
   • Syntax-Semantics Interface

3. Text Representation and Processing Techniques

   • Tokenization, Lemmatization, and Stemming
   • Word Embeddings and Vector Space Models
   • Part-of-Speech Tagging
   • Named Entity Recognition (NER)
   • Syntactic Parsing and Treebanking

4. Machine Learning Approaches in NLP

   • Supervised vs. Unsupervised Learning in NLP
   • Statistical Models and Probabilistic Approaches
   • Hidden Markov Models (HMMs)
   • Neural Networks and Deep Learning in NLP
   • Evaluation Metrics and Methods for NLP Models

---

Part II: Machine Translation Theory and Models

5. Traditional Approaches to Machine Translation

   • Rule-Based Machine Translation (RBMT)
   • Direct Translation Methods and Transfer Models
   • Example-Based Machine Translation (EBMT)
   • Statistical Machine Translation (SMT)
   • Challenges in Rule-Based Systems

6. Statistical Models for Machine Translation

   • Word Alignment Models in Statistical MT
   • Phrase-Based Translation Models
   • Syntax-Based Machine Translation
   • Maximum Entropy Models for MT
   • Training MT Models and Data Acquisition

7. Neural Machine Translation (NMT)

   • Introduction to Neural Networks for MT
   • Sequence-to-Sequence Models and Encoder-Decoder Architectures
   • Attention Mechanisms in Neural Machine Translation
   • Advantages of NMT over Traditional Models
   • End-to-End Training and Optimization in NMT

---

Part III: Advanced Topics in NLP and MT

8. Discourse and Pragmatics in NLP

   • Modeling Discourse and Dialogue Systems
   • Coreference Resolution
   • Speech Acts and Their Role in Understanding Meaning
   • Sentiment Analysis and Opinion Mining
   • Multi-Modal NLP: Integrating Text, Speech, and Visual Data

9. Evaluation of Machine Translation Systems

   • Human Evaluation vs. Automatic Metrics
   • BLEU Score and Other Common Evaluation Metrics
   • Error Analysis in MT
   • Challenges in Evaluating Non-English Languages
   • Case Studies of MT System Performance

10. Challenges and Future Directions in NLP and MT

    • Low-Resource Languages and Their Impact on MT
    • Multilingual and Cross-Lingual Approaches
    • Neural Machine Translation: Current State and Limitations
    • Explainability and Interpretability of MT Models
    • Emerging Trends in NLP and MT Research

---

Conclusion
11. Concluding Remarks: The Future of Natural Language Processing and Machine Translation
– The Integration of NLP and MT in Real-World Applications
– The Role of Artificial Intelligence in Advancing NLP and MT
– Opportunities for Further Research and Development
– Ethical Considerations in NLP and MT Systems
– The Promise of NLP and MT for Global Communication

People also search for The Kluwer international series in engineering and computer science Natural language processing and machine translation SECS 41 1st Edition:

the kluwer international series in engineering and computer science

kluwer navigator inloggen

kluwer pronunciation

engineering and international relations