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# Classifiers
<span className="todo">TODO</span>

| Property  | Description |
| --- | --- |
| Short description  | A document classification algorithm that employs analysis of free text stemming from the abstracts of the publications. The purpose of applying a document classification module is to assign a scientific text to one or more predefined content classes. |
| Authority  | ATHENA Research Center, Greece  |
| Licence  | CC-BY/CC-0  |
| Algorithmic details | The algorithm classifies publication's fulltexts using a Bayesian classifier and weighted terms according to an offline training phase. The training has been done using the following taxonomies: arXiv, MeSH (Medical Subject Headings), ACM, and DDC (Dewey Decimal Classification, or Dewey Decimal System).  |
| Parameters | Publication's identifier and fulltext |
| Limitations | N/A |
| Code repository | https://github.com/openaire/iis/tree/master/iis-wf/iis-wf-referenceextraction/src/main/resources/eu/dnetlib/iis/wf/referenceextraction |
| Environment | Python, madIS (https://github.com/madgik/madis), APSW (https://github.com/rogerbinns/apsw) |
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