Name
Abstract | ||
|---|---|---|
Abstract Entity relation extraction can be applied in the automatic question answering system, digital library and many other fields. However, the previous works on this topic mainly focused on the features from a sentence itself in the data sets, without considering the links between sentences in the corpus. In this paper, we propose a concept model and obtain a new effective spatial feature based on this concept model. The added feature makes our feature space concerning not only the inherent information of the sentence itself, but also the semantic information connection between sentences. At last, we use ELM as the training classifier in entity relation extraction. The experiment result shows that the precision and recall of the relation extraction both have a significant increase, by using the new feature. Also, the use of ELM significantly reduces the time of relation extraction. It has a better performance than the traditional method based on SVM. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/s00521-014-1776-9 | Neural Computing and Applications |
Keywords | Field | DocType |
Concept model,Spatial feature,Entity relation extraction,ELM | Data set,Feature vector,Question answering,Pattern recognition,Computer science,Support vector machine,Precision and recall,Artificial intelligence,Classifier (linguistics),Sentence,Relationship extraction | Journal |
Volume | Issue | ISSN |
27 | 2 | 1433-3058 |
Citations | PageRank | References |
0 | 0.34 | 17 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Huilin Liu | 1 | 0 | 0.34 |
| Chunfeng Jiang | 2 | 0 | 0.34 |
| Chunyan Hu | 3 | 0 | 0.34 |
| li zhang | 4 | 101 | 18.22 |