TY - GEN
T1 - Auto-POS templates and mixed metrics for recognizing terms in scientific literature
AU - You, Hongliang
AU - Zhang, Wei
AU - Shen, Junyi
AU - Yu, Yang
AU - Liu, Ting
PY - 2010
Y1 - 2010
N2 - Automatic Term Recognition (ATR) is an important task for Knowledge Acquisition, which aims at acquiring formalized words which are not recorded in time in the glossary. In recent years, several statistical methods has proved to be effective, and emerging methods such as C-value, NC-Value, TermExtractor has shown great advantages on this task. However, few works have been done on the Metric mixing algorithm that combines those metrics as a whole. In this paper, we first collect part-of-speech tern plates from already-known terms automatically. namely Auto-POS templates. instead of artificial regular expressions, and then we match them with POS strings to acquire candidate terms. Finally we sort those candidates by metric mixing algorithm. Experimental results on IEEE2006-2007 metadata show that the metric mixing algorithm performs better than any separate metrics alone.
AB - Automatic Term Recognition (ATR) is an important task for Knowledge Acquisition, which aims at acquiring formalized words which are not recorded in time in the glossary. In recent years, several statistical methods has proved to be effective, and emerging methods such as C-value, NC-Value, TermExtractor has shown great advantages on this task. However, few works have been done on the Metric mixing algorithm that combines those metrics as a whole. In this paper, we first collect part-of-speech tern plates from already-known terms automatically. namely Auto-POS templates. instead of artificial regular expressions, and then we match them with POS strings to acquire candidate terms. Finally we sort those candidates by metric mixing algorithm. Experimental results on IEEE2006-2007 metadata show that the metric mixing algorithm performs better than any separate metrics alone.
KW - Automatic term recognition
KW - Information extraction
KW - Knowledge acquisition
KW - Text mining
UR - https://www.scopus.com/pages/publications/78650646594
U2 - 10.1109/KAM.2010.5646319
DO - 10.1109/KAM.2010.5646319
M3 - 会议稿件
AN - SCOPUS:78650646594
SN - 9781424480050
T3 - 2010 3rd International Symposium on Knowledge Acquisition and Modeling, KAM 2010
SP - 84
EP - 87
BT - 2010 3rd International Symposium on Knowledge Acquisition and Modeling, KAM 2010
T2 - 2010 3rd International Symposium on Knowledge Acquisition and Modeling, KAM 2010
Y2 - 20 October 2010 through 21 October 2010
ER -