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Challenges in Natural Language Processing Studies in Natural Language Processing 1st Edition by Madeleine Bates, Ralph Weischedel ISBN 0521410150 9780521410151

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Challenges in Natural Language Processing Studies in Natural Language Processing First Edition Madeleine Bates (Editor) Digital Instant Download

Author(s): Madeleine Bates (editor), Ralph M. Weischedel (editor)
ISBN(s): 9780521410151
Edition: First Edition
File Details: PDF, 3.53 MB
Year: 2006
Language: english
SKU: EB-2529802 Categories: , Tags: ,

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Product details:
ISBN 10: 0521410150
ISBN 13: 9780521410151
Author: Madeleine Bates, Ralph Weischedel

Though natural language processing has come far in the past twenty years, the technology has not achieved a major impact on society. Is this because of some fundamental limitation that cannot be overcome? Or because there has not been enough time to refine and apply theoretical work already done? Editors Madeleine Bates and Ralph Weischedel believe it is neither; they feel that several critical issues have never been adequately addressed in either theoretical or applied work, and they have invited capable researchers in the field to do that in Challenges in Natural Language Processing.

Challenges in Natural Language Processing Studies in Natural Language Processing 1st Edition Table of contents:

Chapter 1: Introduction to Natural Language Processing

  • What is Natural Language Processing?
  • Key Areas of NLP: Understanding and Generating Human Language
  • The Evolution of NLP: Past, Present, and Future
  • The Role of AI and Machine Learning in NLP

Chapter 2: Linguistic Challenges in NLP

  • Ambiguity in Natural Language: Lexical and Syntactic
  • Parsing and Sentence Structure: The Difficulty of Understanding Grammar
  • Pragmatics and Discourse Understanding
  • The Problem of Polysemy and Homonymy in Text Processing

Chapter 3: Data Challenges in NLP

  • The Need for Large, Diverse Datasets
  • Handling Noisy Data: Issues in Text Quality and Consistency
  • Data Annotation and Labeling: Challenges in Creating Ground Truth
  • Imbalanced and Sparse Data Problems in NLP

Chapter 4: Computational Challenges in NLP

  • Efficient Algorithms for Language Processing
  • Scalability Issues in Large-Scale Text Processing
  • The Trade-off Between Precision and Recall in NLP Models
  • The Challenge of Real-time NLP in Applications

Chapter 5: Multilingual NLP

  • The Complexity of Processing Multiple Languages
  • Machine Translation: Challenges and Breakthroughs
  • Cross-lingual Understanding and Knowledge Transfer
  • The Role of Language Resources in Multilingual NLP

Chapter 6: Semantic Challenges in NLP

  • Understanding Meaning: Word Sense Disambiguation
  • Word Embeddings and Representations of Meaning
  • Sentence-Level Semantics: Ambiguities and Nuances
  • The Challenge of Context in Language Understanding

Chapter 7: NLP for Low-Resource Languages

  • Addressing the Gap in Resources for Underrepresented Languages
  • Transfer Learning and Cross-lingual Approaches
  • Creating NLP Tools for Smaller Languages and Dialects
  • Ethical Considerations in Developing NLP for Low-Resource Languages

Chapter 8: NLP in Practical Applications

  • Speech Recognition and Natural Language Understanding
  • Question Answering Systems and Chatbots
  • Sentiment Analysis and Opinion Mining
  • Information Retrieval and Search Engines: NLP’s Role
  • NLP in Healthcare: Electronic Health Records and Medical Text Mining

Chapter 9: Ethical and Social Challenges in NLP

  • Bias in NLP Models: Causes and Consequences
  • The Ethical Implications of NLP in Surveillance and Privacy
  • Responsible AI and Fairness in Language Technologies
  • Addressing Discrimination and Injustice in NLP Systems

Chapter 10: Future Directions in NLP

  • The Impact of Deep Learning on NLP Progress
  • The Promise of Transformer Models: BERT, GPT, and Beyond
  • NLP and Its Integration with Other AI Disciplines
  • Challenges of Explainability and Interpretability in NLP Models
  • The Future of NLP in Multimodal and Interactive Systems

Conclusion: Bridging the Gap Between Language and Machines

  • Reflections on the Progress of NLP
  • Overcoming Challenges for Real-world NLP Systems
  • The Role of Collaboration in Advancing NLP Research

Appendices

  • A: Key Resources for NLP Research and Development
  • B: Notable NLP Datasets and Benchmark Tasks
  • C: Glossary of Key NLP Terms

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