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1 An Introduction to Text Analytics.
2 Text Preparation and Similarity Computation.
3 Matrix Factorization and Topic Modeling.
4 Text Clustering.
5 Text Classification: Basic Models.
6 Linear Models for Classification and Regression.
7 Classifier Performance and Evaluation.
8 Joint Text Mining with Heterogeneous Data.
9 Information Retrieval and Search Engines.
10 Language Modeling and Deep Learning.
11 Attention Mechanisms and Transformers.
12 Text Summarization.
13 Information Extraction and Knowledge Graphs.
14 Question Answering.
15 Opinion Mining and Sentiment Analysis.
16 Text Segmentation and Event Detection.
Index |
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Machine Learning for Text(¾çÀ庻 HardCover) | Â÷·ç ¾Æ°¡¸£¿Ð | Springer
Neural Networks and Deep Learning | Â÷·ç ¾Æ°¡¸£¿Ð | Springer
Linear Algebra and Optimization for Machine Learning | Â÷·ç ¾Æ°¡¸£¿Ð | Springer
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