CONTEXT VECTOR CLASSIFICATION - Term Classification with Context Evaluation

Hendrik Schöneberg

2010

Abstract

Automated Deep Tagging heavily relies on a term’s proper recognition. If its syntax is obfuscated by spelling mistakes, OCR errors or typing variants, regular string matching or pattern matching algorithms may not be able to succeed with the classification. Context Vector Tagging is an approach which analyzes term co-occurrence data and represents it in a vector space model, paying specific respect to the source’s language. Utilizing the cosine angle between two context vectors as similarity measure, we propose, that terms with similar context vectors share a similar word class, thus allowing even unknown terms to be classified. This approach is especially suitable to tackle the above mentioned syntactical problems and can support classic string- or pattern-based classificator-algorithms in syntactically challenging environments.

Paper Citation

in Harvard Style

Schöneberg H. (2010). CONTEXT VECTOR CLASSIFICATION - Term Classification with Context Evaluation . In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2010) ISBN 978-989-8425-28-7, pages 387-391. DOI: 10.5220/0003067403870391

in Bibtex Style

@conference{kdir10,
author={Hendrik Schöneberg},
title={CONTEXT VECTOR CLASSIFICATION - Term Classification with Context Evaluation},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2010)},
year={2010},
pages={387-391},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003067403870391},
isbn={978-989-8425-28-7},
}

in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2010)
TI - CONTEXT VECTOR CLASSIFICATION - Term Classification with Context Evaluation
SN - 978-989-8425-28-7
AU - Schöneberg H.
PY - 2010
SP - 387
EP - 391
DO - 10.5220/0003067403870391