- Full Description
This book discusses applications of certain NLP techniques to certain Text Mining tasks, & the use of Text Mining to facilitate NLP. It explores real-world applications of NLP & text-mining algorithms in comprehensive detail, placing emphasis on the description of end-to-end solutions to real problems, & detailing the associated difficulties that must be resolved before the algorithm can be applied & its full benefits realized. Topics & features: • Describes novel & high-impact text mining and/or natural language applications • Points out typical traps in trying to apply NLP to text mining • Surveys related supporting techniques, problem types, & potential technique enhancements • Examines the interaction of text mining & NLP This state-of-the-art, practical volume will be an essential resource for professionals & researchers who wish to learn how to apply text mining & language processing techniques to real world problems. It can also be used as a supplementary text by advanced students in text mining & NLP.
- Table of Contents
Table of Contents
- Extracting Product Features and Opinions from Reviews.
- Extracting Relations from Text.
- Mining Diagnostic Text Reports by Learning to Annotate Knowledge Roles.
- A Case Study in Natural Language Based Web Search.
- Evaluating Self
- explanations in iSTART:Word Matching, Latent Semantic Analysis, and Topic Models.
- Textual Signatures: Identifying Text
- Types Using Latent Semantic Analysis to Measure the Cohesion of Text Structures.
- Automatic Document Separation
- A Combination of Probabilistic Classification and Finite
- State Sequence Modeling.
- Evolving Explanatory Novel Patterns for Semantically
- based Text Mining.
- Handling of Imbalanced Data in Text Classification: Category Based Term Weights.
- Automatic Evaluation of Ontologies.
- Linguistic Computing with UNIX Tools.
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