Sparse Named Entity Classification using Factorization Machines

TL;DR

This paper introduces a matrix factorization approach for named entity classification that effectively handles data sparsity, achieving competitive accuracy with fewer features.

Contribution

It proposes a novel application of factorization machines to improve named entity classification under sparse data conditions.

Findings

Achieves competitive accuracy with fewer features.

Outperforms traditional models on sparse data.

Demonstrates effectiveness of matrix factorization in NLP.

Abstract

Named entity classification is the task of classifying text-based elements into various categories, including places, names, dates, times, and monetary values. A bottleneck in named entity classification, however, is the data problem of sparseness, because new named entities continually emerge, making it rather difficult to maintain a dictionary for named entity classification. Thus, in this paper, we address the problem of named entity classification using matrix factorization to overcome the problem of feature sparsity. Experimental results show that our proposed model, with fewer features and a smaller size, achieves competitive accuracy to state-of-the-art models.