Name
Abstract | ||
|---|---|---|
A key data preparation step in Text Mining, Term Extraction selects the terms, or collocation of words, attached to specific concepts. In this paper, the task of extracting relevant collocations is achieved through a supervised learning algorithm, exploiting a few collocations manually labelled as relevant/irrelevant. The candidate terms are described along 13 standard statistical criteria measures. From these examples, an evolutionary learning algorithm termed Roger, based on the optimization of the Area under the ROC curve criterion, extracts an order on the candidate terms. The robustness of the approach is demonstrated on two |
Year | Venue | Keywords |
|---|---|---|
2005 | Clinical Orthopaedics and Related Research | evolutionary algorithms,real-world domain applications,terminology,considering different domains biology and human resources and different languages english and french. keywords: text mining,roc curve.,human resource,supervised learning,roc curve,evolutionary algorithm,text mining |
Field | DocType | Volume |
Text mining,Evolutionary algorithm,Terminology,Computer science,Robustness (computer science),Preference learning,Artificial intelligence,Data preparation,Machine learning,Terminology extraction,Collocation | Journal | abs/cs/051 |
ISSN | Citations | PageRank |
Proceeedings of Applied Stochastic Models and Data Analysis (2005)
209-219 | 2 | 0.45 |
References | Authors | |
20 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jérôme Azé | 1 | 73 | 15.66 |
| Mathieu Roche | 2 | 96 | 24.74 |
| Yves Kodratoff | 3 | 581 | 172.25 |
| Michèle Sebag | 4 | 1547 | 138.94 |