Name
Abstract | ||
|---|---|---|
Semantic annotation for text is a well-studied topic. However, little contribution has been engaged in the application of short text annotation. In this article, an automatic annotation approach is proposed for such purpose, which annotates short text with semantic labels for question answering systems. In the first step, keywords are extracted from a question and then a semantic label selection module is used to select semantic labels to tag keywords. If there is no appropriate label, Word Net is employed to obtain candidate labels to annotate those keywords by calculating the similarity between each keyword in the question and the concept list in our predefined Tagger Ontology. To improve the accuracy of annotation, we also design a naive Bayesian based method to distinguish multi-senses and assign best semantic labels by referring to historically annotated questions. Preliminary experiments on 6 categories show our approach achieves the precision of 76% in average. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-22810-0_18 | Lecture Notes in Business Information Processing |
Keywords | DocType | Volume |
Text annotation,Similarity,Question answering,Tagger ontology | Conference | 75 |
ISSN | Citations | PageRank |
1865-1348 | 0 | 0.34 |
References | Authors | |
13 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gang Liu | 1 | 83 | 4.93 |
| Zhi Lu | 2 | 257 | 11.74 |
| Tianyong Hao | 3 | 18 | 1.40 |
| Liu Wenyin | 4 | 2531 | 215.13 |