Buch, Englisch, 265 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 1260 g
ISBN: 978-1-84628-175-4
Verlag: Springer
Natural Language Processing and Text Mining not only discusses applications of Natural Language Processing techniques to certain Text Mining tasks, but also the converse, the use of Text Mining to assist NLP. It assembles a diverse views from internationally recognized researchers and emphasizes caveats in the attempt to apply Natural Language Processing to text mining. This state-of-the-art survey is a must-have for advanced students, professionals, and researchers.
Zielgruppe
Professional/practitioner
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Informatik Logik, formale Sprachen, Automaten
- Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsinformatik, SAP, IT-Management
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
- Mathematik | Informatik EDV | Informatik Angewandte Informatik Computeranwendungen in Geistes- und Sozialwissenschaften
- Mathematik | Informatik EDV | Informatik Technische Informatik Hochleistungsrechnen, Supercomputer
- Mathematik | Informatik EDV | Informatik Daten / Datenbanken Datenkompression, Dokumentaustauschformate
- Mathematik | Informatik EDV | Informatik Technische Informatik Systemverwaltung & Management
- Mathematik | Informatik EDV | Informatik Business Application Unternehmenssoftware SAP
- Rechtswissenschaften Öffentliches Recht Verwaltungsrecht Verwaltungspraxis Public Management
Weitere Infos & Material
Overview.- Extracting Product Features and Opinions from Reviews.- Extracting Relations from Text: From Word Sequences to Dependency Paths.- 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.
