Solution
Gathering & Analytics
- Analyze and Structure
- Capture Data and Information
- NLP stack
Capture from Multiple Sources
Internal and External Data
Define Relevant Data Capture Processes
Structuring and Prediction
- Administration
- Taxonomy Management
- Prediction
- NLP Layer
- Build Knowledge Base
Processing / Analysis
NLP & Taxonomy Structuring & Predictive Reasoning
Measurable Values via Non-Linear Inference
Train System to Individual Topics
Interactive Presentation
- Dashboard
- Analysis & Drill Down
Present Results
User-friendly Dashboard
Searched Documents & Calculated Predictions
Implementation Timeline
Set up / Parametrize
1 - 2 days
First Operational Solution
3 - 4 weeks
Continuous Refinement of Solution
Ongoing
The Data Loading and Data Preparation Layer handles all data loading and document pre-processing activities.
Data capture is supported from three different sources: internal data, social media screening and web-data crawling/RSS feeds.
During the data loading process, each loaded document is processed via a complete NLP (Natural Language Processing) stack consisting of:
- Document transformation from any format to pure text format
- Each document is split into individual pages for easier topic location within large text documents (for pdf documents only)
- To improve the search capability a complete Indexation of each word per document is performed
For supporting document analytics the NLP stack further includes:
- Sentiment analysis for document content
- Tokenisation for word and sentence splitting
- Lemmatisation (grouping together the inflected forms of a word so they can be analysed as a single item)
- Stemming (process of reducing inflected (or sometimes derived) words to their word stem, base or root form)
- Tagging and keyword identification
- NER (Named Entity Recognition) for identifying names and companies
- POS (Part of Speech) identification for semantic text analysis
The Learning and Knowledge Capture Layer consists of all services required to train the text analytics models (NLP) to individual knowledge domains, to build individual taxonomy and ontology structures for semantic content categorisation and to build predictive reasoning models using Dydon’s NeuroFuzzy concept.
The Presentation Layer has all generated results presented using parameterizable, easy to use and intuitive dashboards.
The Process
Dydon Technology & Architecture
Java core base with open-source components
Database: MongoDB, NoSQL & REST APIs
AI modules: Apache Lucene/Solr, OpenNLP, Mallet, Fuzzy4j
Fully web-based, cloud and on-premise operation
NLP Stack Components
Get in Touch
- Hechlenberg 17 CH-8704 Herrliberg
- +41 79 457 87 67
- info@dydon.net