Summary of the paper
| Title | Latent Semantic Analysis Models on Wikipedia and TASA |
|---|---|
| Authors | Dan Stefanescu, Rajendra Banjade and Vasile Rus |
| Abstract | This paper introduces a collection of freely available Latent Semantic Analysis models built on the entire English Wikipedia and the TASA corpus. The models differ not only on their source, Wikipedia versus TASA, but also on the linguistic items they focus on: all words, content-words, nouns-verbs, and main concepts. Generating such models from large datasets (e.g. Wikipedia), that can provide a large coverage for the actual vocabulary in use, is computationally challenging, which is the reason why large LSA models are rarely available. Our experiments show that for the task of word-to-word similarity, the scores assigned by these models are strongly correlated with human judgment, outperforming many other frequently used measures, and comparable to the state of the art. |
| Topics | Statistical and Machine Learning Methods, Tools, Systems, Applications |
Full paper  |
Latent Semantic Analysis Models on Wikipedia and TASA |
| Bibtex | @InProceedings{STEFANESCU14.403, author = {Dan Stefanescu and Rajendra Banjade and Vasile Rus}, title = {Latent Semantic Analysis Models on Wikipedia and TASA}, booktitle = {Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)}, year = {2014}, month = {may}, date = {26-31}, address = {Reykjavik, Iceland}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Hrafn Loftsson and Bente Maegaard and Joseph Mariani and Asuncion Moreno and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, isbn = {978-2-9517408-8-4}, language = {english} } |