Name
Abstract | ||
|---|---|---|
One major problem in text mining and seman- tic retrieval is that detected entity mentions have to be assigned to the true underlying entity. The ambiguity of a name results from both the pol- ysemy and synonymy problem, as the name of a unique entity may be written in variant ways and different unique entities may have the same name. The term "bush" for instance may refer to a woody plant, a mechanical fixing, a noctur- nal primate, 52 persons and 8 places covered in Wikipedia and thousands of other persons. For the first time, according to our knowledge we apply a kernel entity resolution approach to the German Wikipedia as reference for named enti- ties. We describe the context of named entities in Wikipedia and the context of a detected name phrase in a new document by a context vector of relevant features. These are designed from au- tomatically extracted topic indicators generated by an LDA topic model. We use kernel classi- fiers, e.g. rank classifiers, to determine the right matching entity but also to detect uncovered en- tities. In comparison to a baseline approach us- ing only text similarity the addition of topics ap- proach gives a much higher f-value, which is comparable to the results published for English. It turns out that the procedure also is able to de- tect with high reliability if a person is not covered by the Wikipedia. |
Year | Venue | DocType |
|---|---|---|
2009 | LWA | Conference |
Citations | PageRank | References |
5 | 0.53 | 14 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anja Pilz | 1 | 32 | 2.83 |
| Gerhard Paass | 2 | 1136 | 83.63 |