Name
Abstract | ||
|---|---|---|
The complexity of chemical substance names makes it difficult to fully describe chemical substances using just several keywords. We usually find related information through search engines or look up an online chemical dictionary. However, the chemical material names used in academy usually translated from English, and the same chemicals often have many different aliases. This English Chinese translation creates many problems when querying information for chemicals. Recent studies have proposed to use Normalized Google Distance (NGD) to determine semantic relevance between two words. Therefore, this study proposes to find alias based on NGD with two methods, namely, novel and category affixed methods. The Experimental results show that the latter method can derive better result. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-41009-8_13 | ADVANCES IN SWARM INTELLIGENCE, ICSI 2016, PT II |
Keywords | Field | DocType |
Normalized google distance (NGD),Text mining,Chemical material name | Normalized Google distance,Alias,Search engine,Information retrieval,Computer science,Chemical substance,Semantic relevance,Artificial intelligence,Machine learning | Conference |
Volume | ISSN | Citations |
9713 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ching-Yi Chen | 1 | 0 | 0.34 |
| pingyu hsu | 2 | 4 | 2.81 |
| Ming-Shien Cheng | 3 | 3 | 7.16 |
| Jui Yi Chung | 4 | 0 | 0.34 |
| Ming Chia Hsu | 5 | 0 | 1.35 |