For an Automatic Analysis of Discourse: A Linguistic Perspective

Introduction

In the field of natural language processing (NLP), discourse analysis is a crucial aspect of understanding the structure and meaning of text. It involves analyzing the relationships between sentences and larger units of text, such as paragraphs and chapters. Traditional approaches to discourse analysis have relied on manual annotation by human experts, which can be time-consuming and subjective.

Automatic Discourse Analysis

Automatic discourse analysis aims to overcome these limitations by developing computational methods for analyzing discourse. This book provides a comprehensive overview of the state-of-the-art in automatic discourse analysis, covering a wide range of topics, including:

• Discourse segmentation: Identifying the boundaries between different units of discourse, such as sentences, paragraphs, and chapters.
• Discourse coherence: Analyzing the relationships between different parts of a discourse to determine how they are connected and make sense together.
• Discourse anaphora: Identifying and resolving anaphoric references, such as pronouns and definite noun phrases, to determine what they refer to in the discourse.
• Discourse deixis: Identifying and interpreting deictic expressions, such as demonstratives and spatial adverbs, to determine their meaning in the context of the discourse.
• Discourse modality: Analyzing the expression of modality in discourse, such as possibility, necessity, and obligation, to determine the speaker's attitude towards the information being presented.

Applications of Automatic Discourse Analysis

Automatic discourse analysis has a wide range of applications, including:

• Machine translation: Improving the quality of machine translation by preserving discourse structure and coherence.
• Summarization: Generating concise summaries of text by identifying the most important information and preserving discourse structure.
• Question answering: Answering questions about text by understanding the discourse structure and relationships between different parts of the text.
• Information extraction: Extracting relevant information from text by identifying the main topics and relationships between them.
• Sentiment analysis: Determining the sentiment or opinion expressed in text by analyzing the discourse structure and the use of language.

Conclusion

Automatic discourse analysis is a rapidly growing field with the potential to revolutionize the way we interact with text. This book provides a comprehensive overview of the state-of-the-art in automatic discourse analysis, making it an essential resource for researchers, practitioners, and students interested in this field.

Why You Should Buy This Book

If you are interested in natural language processing, discourse analysis, or any of the applications mentioned above, then this book is a must-have for your library. It provides a comprehensive and up-to-date overview of the field, making it an invaluable resource for anyone working in this area.

Here are a few reasons why you should buy this book:

• Comprehensive coverage: This book covers a wide range of topics in automatic discourse analysis, from discourse segmentation to discourse modality.
• In-depth analysis: Each chapter provides a detailed analysis of a particular topic, including the state-of-the-art in research and the challenges that still need to be addressed.
• Clear and concise: The book is written in a clear and concise style, making it easy to understand even for those who are new to the field.
• Extensive references: Each chapter includes an extensive list of references, providing readers with a starting point for further research.

If you are interested in learning more about automatic discourse analysis, then this book is the perfect resource for you. Order your copy today!