Evaluating and applying deep learning-based multilingual named entity recognition
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초록
Named entities (NEs) are words or phrases that are the referent of proper nouns with distinct meanings, such as a person (PER), location (LOC), or organization (ORG). Named entity recognition (NER) is a task that aims to locate and classify NEs in text into pre-defined categories such as PER, LOC, and ORG. NER is a well-studied area in natural language processing. Nevertheless, multilingual NER tasks that treat various languages in different language families with the same architecture are rarely investigated. In this paper, we discuss an NER system designed to deal with multiple languages. For our experiments, we develop Korean and Chinese NER systems. The experimental results show that the overall performance of the system in terms of the F-measure is 73.06% (Korean) and 40.67% (Chinese). Concurrently, the performance of NE detection has an accuracy of more than 94% for both Korean and Chinese. We apply the NER system conceptually for marine term extraction because the term extraction is similar to NER in that it detects words or phrases with specific meanings used in a particular context.
Abstract
Named entities (NEs) are words or phrases that are the referent of proper nouns with distinct meanings, such as a person (PER), location (LOC), or organization (ORG). Named entity recognition (NER) is a task that aims to locate and classify NEs in text into pre-defined categories such as PER, LOC, and ORG. NER is a well-studied area in natural language processing. Nevertheless, multilingual NER tasks that treat various languages in different language families with the same architecture are rarely investigated. In this paper, we discuss an NER system designed to deal with multiple languages. For our experiments, we develop Korean and Chinese NER systems. The experimental results show that the overall performance of the system in terms of the F-measure is 73.06% (Korean) and 40.67% (Chinese). Concurrently, the performance of NE detection has an accuracy of more than 94% for both Korean and Chinese. We apply the NER system conceptually for marine term extraction because the term extraction is similar to NER in that it detects words or phrases with specific meanings used in a particular context.
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