For Nick's current work at Atomic Intelligence, see here.


Consulting services in natural language processing, text mining, machine learning and search.


A growing number of applications use text analysis and language processing.

Contextual advertising
Mobile services
Social networking
Legal e-discovery
IT log file monitoring
Customer comments and feedback
Cloud storage services
Enterprise search
Extracting financial information
E-commerce product and price extraction

Each application requires a blend of techniques.

Which techniques work best for each application? And which modules or algorithms to use for each approach?

A consultant can get you up to speed fast.
Help you work out what to do and what not do -- avoid the pitfalls and blind alleys
Quickly deploy best-of-breed algorithms

Consulting can focus on one or more areas.

Training: Getting your team up to speed.
Goals: Business goals, technical goals, user needs
Feasibility: What is possible?
Evaluation of algorithms: How to do it?
Data experiments: Explore and test the approaches.
Evaluation of components: Whether to build, re-use, or buy.
Technology roadmap: How to roll out the solution.
Recruitment: Finding the right staff.
Implementation: Actually getting there.

From Wikipedia entry on text mining:

Text mining, sometimes alternately referred to as text data mining, roughly equivalent to text analytics, refers generally to the process of deriving high-quality information from text. High-quality information is typically derived through the dividing of patterns and trends through means such as statistical pattern learning. Text mining usually involves the process of structuring the input text (usually parsing, along with the addition of some derived linguistic features and the removal of others, and subsequent insertion into a database), deriving patterns within the structured data, and finally evaluation and interpretation of the output. 'High quality' in text mining usually refers to some combination of relevance, novelty, and interestingness. Typical text mining tasks include text categorization, text clustering, concept/entity extraction, production of granular taxonomies, sentiment analysis, document summarization, and entity relation modeling (i.e., learning relations between named entities).