Welcome to NaCTeM

National Centre for Text Mining — Text Mining Tools and Text Mining Services

Welcome to NaCTeM

The National Centre for Text Mining (NaCTeM) is the first publicly-funded text mining centre in the world. We provide text mining services in response to the requirements of the UK academic community. NaCTeM is operated by the University of Manchester.

On our website, you can find pointers to sources of information about text mining such as links to

• text mining services provided by NaCTeM
• software tools, both those developed by the NaCTeM team and by other text mining groups
• seminars, general events, conferences and workshops
• tutorials and demonstrations
• text mining publications

Let us know if you would like to include any of the above in our website.

What text mining can do for you

Text mining offers a solution to the challenge of 'data deluge', information overload and information overlook. For more information, please see:

• NaCTeM Brochure,
• Text Mining Briefing Paper,
• National Centre for Text Mining: an introduction to tools for researchers,
• Vision for the Future,
• Mining Biomedical Literature.
• Event extraction for systems biology by text mining the literature
• Supporting the education evidence portal via text mining

NaCTeM has developed text mining services and service exemplars for the UK academic community. Our services are underpinned by a number of generic natural language processing tools:

• TerMine is a Term Management System which identifies key phrases in text.
• AcroMine is an acronym dictionary which can be used to find distinct expanded forms of acronyms from MEDLINE.
• Kleio is an advanced information retrieval system providing knowledge enriched searching for biomedicine.
• FACTA+ is a MEDLINE search engine for finding associations between biomedical concepts.
• IRS facilitates advanced searching of documents by making use of added value features extracted from full texts using NaCTeM text mining tools.
• MEDIE uses semantic search to retrieve biomedical correlations from MEDLINE.
• Info-PubMed uses a gene/protein dictionary and deep parsing to understand protein interactions [Firefox Required].