Natural Language Processing
A Machine Learning Perspective
With a machine learning approach and less focus on linguistic details, this gentle introduction to natural language processing develops fundamental mathematical and deep learning models for NLP under a unified framework. NLP problems are systematically organised by their machine learning nature, including classification, sequence labelling, and sequence-to-sequence problems. Topics covered include statistical machine learning and deep learning models, text classification and structured prediction models, generative and discriminative models, supervised and unsupervised learning with latent variables, neural networks, and transition-based methods.…
- Add bookmark
- Systematically discusses natural language processing from a machine learning perspective, delivering a deeper mathematical understanding of NLP solutions. Students can then harness this knowledge to solve NLP tasks and build better NLP models.
- Provides running examples, figures, and high-level description throughout, allowing students to absorb machine learning concepts and proofs in a meaningful way
- 200 end-of-chapter questions and 80 illustrations provided throughout, reinforce student understanding
- Features in-depth discussion of deep learning methods and NLP
- Establishes a strong correlation between deep learning and linear models for NLP, smoothing the steep learning curve for students as they draw connections between these concepts in a unified framework
- Explains the reasoning behind NLP models so that engineers will be able to better use, tailor, and even improve them
About the book
- DOI https://doi.org/10.1017/9781108332873
- Subjects Artificial Intelligence and Natural Language Processing,Computational Linguistics,Computer Science,Language and Linguistics
- Format: Hardback
- Publication date: 04 February 2021
- ISBN: 9781108420211
- Dimensions (mm): 246 x 189 mm
- Weight: 1.19kg
- Page extent: 484 pages
- Availability: In stock
- Format: Digital
- Publication date: 16 December 2020
- ISBN: 9781108332873
Review the options below to login to check your access.
Log in with your Cambridge Higher Education account to check access.
There are no purchase options available for this title.
Have an access code?
To redeem an access code, please log in with your personal login.
If you believe you should have access to this content, please contact your institutional librarian or consult our FAQ page for further information about accessing our content.