Summary of the paper
| Title | A Random Graph Walk based Approach to Computing Semantic Relatedness Using Knowledge from Wikipedia |
|---|---|
| Authors | Ziqi Zhang, Anna Lisa Gentile, Lei Xia, José Iria and Sam Chapman |
| Abstract | Determining semantic relatedness between words or concepts is a fundamentalprocess to many Natural Language Processing applications. Approaches for thistask typically make use of knowledge resources such as WordNet and Wikipedia.However, these approaches only make use of limited number of features extractedfrom these resources, without investigating the usefulness of combining variousdifferent features and their importance in the task of semantic relatedness. Inthis paper, we propose a random walk model based approach to measuring semanticrelatedness between words or concepts, which seamlessly integrates variousfeatures extracted from Wikipedia to compute semantic relatedness. Weempirically study the usefulness of these features in the task, and prove thatby combining multiple features that are weighed according to their importance,our system obtains competitive results, and outperforms other systems on somedatasets. |
| Language | Textual Entailment and Paraphrasing |
| Topics | Semantics, Word Sense Disambiguation, Textual Entailment and Paraphrasing |
Full paper  |
A Random Graph Walk based Approach to Computing Semantic Relatedness Using Knowledge from Wikipedia |
| Bibtex | @InProceedings{ZHANG10.292, author = {Ziqi Zhang, Anna Lisa Gentile, Lei Xia, José Iria and Sam Chapman}, title = {A Random Graph Walk based Approach to Computing Semantic Relatedness Using Knowledge from Wikipedia}, booktitle = {Proceedings of the Seventh conference on International Language Resources and Evaluation (LREC'10)}, year = {2010}, month = {may}, date = {19-21}, address = {Valletta, Malta}, editor = {Nicoletta Calzolari (Conference Chair), Khalid Choukri, Bente Maegaard, Joseph Mariani, Jan Odjik, Stelios Piperidis, Mike Rosner, Daniel Tapias}, publisher = {European Language Resources Association (ELRA)}, isbn = {2-9517408-6-7}, language = {english} } |