Name
Abstract | ||
|---|---|---|
Large scale unstructured text records are stored in text attributes in databases and information systems, such as scientific citation records or news highlights. Approximate string matching techniques for full text retrieval, e.g., edit distance and cosine similarity, can be adopted for unstructured text record similarity evaluation. However, these techniques do not show the best performance when applied directly, owing to the difference between unstructured text records and full text. In particular, the information are limited in text records of short length, and various information formats such as abbreviation and data missing greatly affect the record similarity evaluation. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.ins.2014.08.007 | Information Sciences |
Keywords | Field | DocType |
Similarity measure,Probabilistic correlation,Text record | Edit distance,Information system,Cosine similarity,Similarity measure,Information retrieval,Computer science,Correlation,Approximate string matching,Probabilistic logic,Document retrieval | Journal |
Volume | ISSN | Citations |
289 | 0020-0255 | 6 |
PageRank | References | Authors |
0.55 | 27 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shaoxu Song | 1 | 259 | 31.50 |
| Han Zhu | 2 | 215 | 8.48 |
| Lei Chen | 3 | 6239 | 395.84 |