Name
Abstract | ||
|---|---|---|
A semantic class is a collection of items (words or phrases) which have semantically peer or sibling relationship. This paper studies the employment of topic models to automatically construct semantic classes, taking as the source data a collection of raw semantic classes (RASCs), which were extracted by applying predefined patterns to web pages. The primary requirement (and challenge) here is dealing with multi-membership: An item may belong to multiple semantic classes; and we need to discover as many as possible the different semantic classes the item belongs to. To adopt topic models, we treat RASCs as "documents", items as "words", and the final semantic classes as "topics". Appropriate preprocessing and postprocessing are performed to improve results quality, to reduce computation cost, and to tackle the fixed-k constraint of a typical topic model. Experiments conducted on 40 million web pages show that our approach could yield better results than alternative approaches. |
Year | Venue | Keywords |
|---|---|---|
2009 | ACL/IJCNLP | pattern-based semantic class discovery,different semantic class,employing topic model,web page,million web page,multiple semantic class,final semantic class,results quality,topic model,semantic class,typical topic model,raw semantic class,information retrieval,web pages |
Field | DocType | Volume |
Semantic similarity,Web page,Semantic Web Stack,Information retrieval,Computer science,Artificial intelligence,Semantic grid,Natural language processing,Topic model,Social Semantic Web,Semantic computing,Semantic role labeling | Conference | P09-1 |
Citations | PageRank | References |
16 | 0.69 | 21 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Huibin Zhang | 1 | 55 | 3.20 |
| Mingjie Zhu | 2 | 89 | 4.32 |
| Shuming Shi | 3 | 620 | 58.27 |
| Ji-Rong Wen | 4 | 4431 | 265.98 |