May 1, 2024
4 minute read
Vector space models (VSMs) are a mathematical framework for representing text as vectors of numbers. They are used in a variety of natural language processing (NLP) tasks, such as text classification, text clustering, and information retrieval. VSMs are based on the idea that the meaning of a text can be represented by the words that it contains, and that the relationships between words can be captured by the distances between their vectors.
Vector Space Models
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Find a path to becoming a Vector Space Models. Learn more at:
OpenCourser.com/topic/2j17go/vector
Reading list
We've selected eight books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Vector Space Models.
This comprehensive textbook provides a broad overview of information retrieval, covering both the theoretical foundations and practical applications of vector space models. It is written by leading researchers in the field and is suitable for both undergraduate and graduate students.
Provides a comprehensive survey of information retrieval, including a chapter on vector space models. It is written by a leading researcher in the field and is suitable for both undergraduate and graduate students.
Provides a comprehensive overview of deep learning techniques for natural language processing, including vector space models. It is written by leading researchers in the field and is suitable for both undergraduate and graduate students.
Provides a comprehensive overview of text mining, including a chapter on vector space models. It is written by leading researchers in the field and is suitable for both undergraduate and graduate students.
Provides a practical introduction to search engines, including the use of vector space models. It is written by leading researchers in the field and is suitable for both undergraduate and graduate students.
Provides a comprehensive overview of clustering and information retrieval, including a chapter on vector space models. It is written by a leading researcher in the field and is suitable for both undergraduate and graduate students.
Covers a wide range of information retrieval topics, including vector space models. It is written in a clear and concise style, and it is suitable for both undergraduate and graduate students.
Provides a comprehensive overview of natural language processing, including a chapter on vector space models. It is written in a clear and concise style, and it is suitable for both undergraduate and graduate students.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/2j17go/vector
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