forked from D-Net/openaire-graph-docs
25 lines
1.1 KiB
Markdown
25 lines
1.1 KiB
Markdown
---
|
|
sidebar_position: 5
|
|
---
|
|
|
|
# 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) |
|
|
| |
|
|
|
|
|
|
|
|
|
|
|
|
|