TY - GEN
T1 - Domain-General Versus Domain-Specific Named Entity Recognition
T2 - 13th Multi-disciplinary International Conference on Artificial Intelligence 2019
AU - Lim, Cheng Yang
AU - Tan, Ian K. T.
AU - Selvaretnam, Bhawani
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019/10/21
Y1 - 2019/10/21
N2 - Named entity recognition (NER) seeks to identify and classify named entities within bodies of text into language categories such as nouns, that are reflective of locations, organizations, and people. As it is language dependent, the approach taken for most NER systems are domain-general, meaning that they are designed based on a language and not on a specific targeted domain. With current usage of non-formal languages on social media, this instigates the need to compare the performance of domain-general and domain specific NERs. A domain specific NER (vehicle traffic domain), TEXT, is described and the performance of domain-general NER versus TEXT is compared. The results of the evaluation show that the performance of domain-specific NER significantly outperforms domain-general NER. The domain-general NER could only perform adequately for common scenarios.
AB - Named entity recognition (NER) seeks to identify and classify named entities within bodies of text into language categories such as nouns, that are reflective of locations, organizations, and people. As it is language dependent, the approach taken for most NER systems are domain-general, meaning that they are designed based on a language and not on a specific targeted domain. With current usage of non-formal languages on social media, this instigates the need to compare the performance of domain-general and domain specific NERs. A domain specific NER (vehicle traffic domain), TEXT, is described and the performance of domain-general NER versus TEXT is compared. The results of the evaluation show that the performance of domain-specific NER significantly outperforms domain-general NER. The domain-general NER could only perform adequately for common scenarios.
KW - Domain-general
KW - Domain-specific
KW - Information extraction
KW - Named Entity Recognition
KW - Traffic
UR - https://www.scopus.com/pages/publications/85076254730
U2 - 10.1007/978-3-030-33709-4_21
DO - 10.1007/978-3-030-33709-4_21
M3 - Conference contribution
AN - SCOPUS:85076254730
SN - 9783030337087
T3 - Lecture Notes in Computer Science
SP - 238
EP - 246
BT - Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2019
A2 - Chamchong, Rapeeporn
A2 - Wong, Kok Wai
PB - Springer
Y2 - 17 November 2019 through 19 November 2019
ER -