Name
Title | ||
|---|---|---|
A Promising Combination Of Approaches For Solving Complex Text Classification Tasks: Application To The Classification Of Scientific Papers Into Patents Classes |
Abstract | ||
|---|---|---|
This paper focuses on a subtask of the QUAERO1 research program, a major innovating research project related to the automatic processing of multimedia and multilingual content. The objective discussed in this paper is to propose a new method for the classification of scientific papers, developed in the context of an international patents classification plan related to the same field. The practical purpose of this work is to provide an assistance tool to experts in their task of evaluation of the originality and novelty of a patent, by offering to the latter the most relevant scientific citations. This issue raises new challenges in categorisation research as the patent classification plan is not directly adapted to the structure of scientific documents, classes have high citation or cited topic and that there is not always a balanced distribution of the available examples within the different learning classes. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1504/IJKL.2014.067187 | INTERNATIONAL JOURNAL OF KNOWLEDGE AND LEARNING |
Keywords | Field | DocType |
supervised classification, patents, KNN, K-nearest-neighbours, association rules, feature selection, feature maximisation metric | Library classification,Data science,Research program,Feature selection,Information retrieval,Computer science,Citation,Patent classification,Originality,Association rule learning,Novelty | Journal |
Volume | Issue | ISSN |
9 | 1-2 | 1741-1009 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kafil Hajlaoui | 1 | 23 | 3.92 |
| Jean-Charles Lamirel | 2 | 171 | 28.79 |
| Pascal Cuxac | 3 | 65 | 9.65 |