出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Named Entity Recognition (NER) is the fundamental subtask of information extraction
systems that labels elements into categories such as persons, organizations or locations. The
task of NER is to detect and classify words that are parts of sentences. This paper describes a
statistical approach to modeling NER in Dari language. Dari and Pashto are low resources languages,
spoken as official languages in Afghanistan. Unlike other languages, named entity detection
approaches differ in Dari. Since in Dari language there is no capitalization for identifying named
entities. We seek to bridge the gap between Dari linguistic structure and supervised learning model
that predict the sequences of words paired with a sequence of tags as outputs. Dari corpus was
developed from the collection of news, reports and articles based on the original orthographic
structure of the Dari language. The experimental result of named entity recognition performance
presents 94% accuracy.
关键词：Natural Language Processing (NLP); Hidden Markov Model (HMM); Named Entity
Recognition (NER); Part;of;Speech (POS) Tagging;
