Sparse Named Entity Classification using Factorization Machines
This work addresses the data sparsity issue in named entity classification, which is a domain-specific problem for natural language processing, but it is incremental as it builds on existing methods.
The paper tackles the problem of named entity classification by addressing feature sparsity using matrix factorization, achieving competitive accuracy to state-of-the-art models with fewer features and smaller size.
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.