# Semantic classifier approach to document classification

## TL;DR

This paper introduces SEMCOM, a novel document classification method that effectively bridges the semantic gap between training and application data, outperforming traditional classifiers and ensembles.

## Contribution

The paper presents SEMCOM, a new approach combining categorization with classifiers to improve document classification accuracy across different datasets.

## Key findings

- SEMCOM outperforms classical classification methods.
- It effectively bridges the semantic gap in textual data.
- The approach enhances classification robustness across datasets.

## Abstract

In this paper we propose a new document classification method, bridging discrepancies (so-called semantic gap) between the training set and the application sets of textual data. We demonstrate its superiority over classical text classification approaches, including traditional classifier ensembles. The method consists in combining a document categorization technique with a single classifier or a classifier ensemble (SEMCOM algorithm - Committee with Semantic Categorizer).