We implement NLP techniques to understand both the user’s natural language query and the enterprise’s content to deliver the most relevant insights.
Raw language processing
As raw data varies from different sources, we bring content processing services to ensure your data is enriched for the highest-quality results.
- Extract text content
- Remove common, irrelevant sections
- Extract coded metadata
- Token extraction, normalization, and cleansing
- Phrase extraction
Text mining and text extraction
Often, the natural language content is not conveniently tagged. Text mining, text extraction, or possibly full-up NLP can be used to extract useful insights from this content.
- Entity extraction – such as companies, people, locations, etc.
- Content categorization – to conduct sentiment analysis
- Content clustering – to identify the main topics and discover new topics
- Fact extraction – to fill databases with structured information for analysis, visualization, and alerting
- Relationship extraction – to fill out graph databases to explore real-world relationships
Statistical language processing
To provide a general understanding of the document as a whole.
- Clustering
- Categorization
- Similarity
- Topic analysis
- Word clouds
- Summarization
