Text & Natural Language Processing¶
A large amount of information is available in the form of text. For example, tweets, emails, survey responses, product reviews and so forth contain information that is written in natural language.
The goal of working with text is to convert it into data that can be useful for analysis. Some applications of text analysis include: sentiment analysis, named entity recognition, summarization, and so forth.
DSS provides numerous NLP capabilities, either through native processing, through locally-running models, or leveraging third party APIs.
- Language Detection
- Named Entities Extraction
- Sentiment Analysis
- Translation
- Text summarization
- Key phrase extraction
- Ontology Tagging
- Spell checking
- OpenAI GPT
- Machine Learning with Text features
- Text extraction
- OCR (Optical Character recognition)
- Speech-to-Text
- Text cleaning
- Text Embedding
- NLP using AWS APIs
- NLP using Azure APIs
- NLP with Crowlingo API
- NLP using Deepl API
- NLP using Google APIs
- NLP with MeaningCloud API