А. A popular categorization is based on the the IPTC categorization scheme, suitable for news articles. Many of our competitors are basing their categorization on the IPTC standard. Advantage: describes well news. Disadvantage: it is a flat categorization, does not have levels. Too targeted to news.
To date, the categorizations comprizes 17 broad topics: Arts_Culture_Entertainment, Conflicts_War_Peace,Crime_Law_Justice, Disaster_Accident, Economy_Business_Finance, Education, Environment, Health, Human_Interest, Labor, Lifestyle_Leisure, Politics, Religion_Belief, Science_Technology, Society, Sports, Weather.
For the next development versions, it is possible (and desired) to extend the categorization scheme by appending sub-categories, identical or inspired by the IPTC. More refined categories can result in a more specific description of the topic of the document, but can raise problems with model fitting.
B. For unsupervised approaches, where the categories are not specified apriori, one can use ontology terms, such as dbpedia categories, of various degrees of specificity.
A. A corpus consisting of long abstracts from dbpedia of articles that belong to the 17 IPTC categories, as shown here: https://confluence.ontotext.com/display/GSC/Document+Classification+Corpora . The corpus is available in EN and BG.
B. One corpus has been obtained form the ACM classification system . It consists of titles and abstracts of scientific papers published by ACM. Here is the file. Each row starts with CCS, which is the root category of the tree. Tab-separated records specify parths in the tree. The leaves are articles, given as title and abstract, tab-separated. Example of articles in category:
CCS -> General and reference -> Cross-computing tools and techniques -> Metrics
|Measured impact of crooked traceroute||Data collected using traceroute-based algorithms underpins research into the Internet's router-level topology, though it is possible to infer false links from this data...|
|Semantic mining on customer survey||Business intelligence aims to support better business decision-making. Customer survey is priceless asset for intelligent business decision-making....|
|Predicting software complexity by means of evolutionary testing||One characteristic that impedes software from achieving good levels of maintainability is the increasing complexity of software...|
|Runtime monitoring of software energy hotspots||GreenIT has emerged as a discipline concerned with the optimization of software solutions with regards to their energy consumption....|
|Structured merge with auto-tuning: balancing precision and performance||Software-merging techniques face the challenge of finding a balance between precision and performance...|