Name
Abstract | ||
|---|---|---|
The text mining tools proposed in this paper extract association rules from a set of specialized and homogeneous texts (corpus). This tool is built in several steps and, at each of them, the expert plays a fundamental role. The first step extracts the terms from the corpus, and clusters them in classes by semantic similarity, associating each class to a concept meaningful to a field expert. Using the knowledge thus obtained, the corpus generates a table of concept frequencies in the texts. Next, we discretize the values of this table, and finally we are able to extract association rules among the concept occurrences. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1007/978-3-540-39985-8_10 | INTELLIGENT INFORMATION PROCESSING AND WEB MINING |
Keywords | Field | DocType |
association rule | Semantic similarity,Data mining,Text mining,Information retrieval,Homogeneous,Computer science,Association rule learning,Artificial intelligence,Natural language processing | Conference |
ISSN | Citations | PageRank |
1615-3871 | 6 | 0.55 |
References | Authors | |
11 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mathieu Roche | 1 | 96 | 24.74 |
| Jérôme Azé | 2 | 73 | 15.66 |
| Oriane Matte-tailliez | 3 | 18 | 3.23 |
| Yves Kodratoff | 4 | 581 | 172.25 |