Turkish Named Entity Discovery Based on Termsets
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Tarih
2019
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
IEEE
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Named Entity Recognition (NER) is a subtask of the information extraction process and aims to discover named entities in unstructured texts. Previous studies on NER mostly use statistical machine learning models instead of using classifiers since solving this problem as a classification task requires to deal with quite high dimensional and sparse vector spaces. In this paper, we take NER as a classical text classification problem and extract nominal features from each token in the unstructured text sequence. We convert each token to a document transaction and then, we use frequent termset mining to extract termset features and apply termset weighting to classify named entities. Therefore we deal with lower dimensional feature spaces. Our experimental results obtained on a large Turkish dataset show that frequent termsets and their weighting scheme can be used in NER task.
Açıklama
4th International Conference on Computer Science and Engineering (UBMK) -- SEP 11-15, 2019 -- Samsun, TURKEY
Anahtar Kelimeler
Named entity recognition, frequent itemset min-ing, termsets, text classification
Kaynak
2019 4th International Conference on Computer Science and Engineering (Ubmk)
WoS Q Değeri
N/A
Scopus Q Değeri
0
