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文件名称：Machine Learning for Text
文件大小：6.14MB
文件格式：PDF
更新时间：2021-08-19 17:55:01
机器学习
The rich area of text analytics draws ideas from information retrieval, machine learning,
and natural language processing. Each of these areas is an active and vibrant field in its
own right, and numerous books have been written in each of these different areas. As a
result, many of these books have covered some aspects of text analytics, but they have not
covered all the areas that a book on learning from text is expected to cover.
At this point, a need exists for a focussed book on machine learning from text. This
book is a first attempt to integrate all the complexities in the areas of machine learning,
information retrieval, and natural language processing in a holistic way, in order to create
a coherent and integrated book in the area. Therefore, the chapters are divided into three
categories:
1. Fundamental algorithms and models: Many fundamental applications in text analytics,
such as matrix factorization, clustering, and classification, have uses in domains
beyond text. Nevertheless, these methods need to be tailored to the specialized characteristics
of text. Chapters 1 through 8 will discuss core analytical methods in the
context of machine learning from text.
2. Information retrieval and ranking: Many aspects of information retrieval and ranking
are closely related to text analytics. For example, ranking SVMs and link-based
ranking are often used for learning from text. Chapter 9 will provide an overview of
information retrieval methods from the point of view of text mining.
3. Sequence- and natural language-centric text mining: Although multidimensional representations
can be used for basic applications in text analytics, the true richness of
the text representation can be leveraged by treating text as sequences. Chapters 10
through 14 will discuss these advanced topics like sequence embedding, deep learning,
information extraction, summarization, opinion mining, text segmentation, and event
extraction.