While the framework discussed in this chapter worked very well for the data that we have in hand, this might not be case when the dataset is small, or if the data is imbalanced.
Scope for improvement
by Rajalingappaa Shanmugamani, Rajesh Arumugam
Hands-On Natural Language Processing with Python
- Title Page
- Copyright and Credits
- Packt Upsell
- Foreword
- Contributors
- Preface
- Getting Started
- Basic concepts and terminologies in NLP
- Applications of NLP
- Analyzing sentiment
- Recognizing named entities
- Linking entities
- Translating text
- Natural Language Inference
- Semantic Role Labeling
- Relation extraction
- SQL query generation, or semantic parsing
- Machine Comprehension
- Textual Entailment
- Coreference resolution
- Searching
- Question answering and chatbots
- Converting text-to-voice
- Converting voice-to-text
- Speaker identification
- Spoken dialog systems
- Other applications
- Summary
- Text Classification and POS Tagging Using NLTK
- Deep Learning and TensorFlow
- Semantic Embedding Using Shallow Models
- Text Classification Using LSTM
- Searching and DeDuplicating Using CNNs
- Named Entity Recognition Using Character LSTM
- Text Generation and Summarization Using GRUs
- Question-Answering and Chatbots Using Memory Networks
- The Question-Answering task
- Memory networks for Question-Answering
- Extending memory networks for dialog modeling
- Summary
- Machine Translation Using the Attention-Based Model
- Speech Recognition Using DeepSpeech
- Text-to-Speech Using Tacotron
- Deploying Trained Models
- Other Books You May Enjoy
Scope for improvement
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