Summary of the paper
| Title | Choosing which to Use? A Study of Distributional Models for Nominal Lexical Semantic Classification |
|---|---|
| Authors | Lauren Romeo, Gianluca Lebani, Núria Bel and Alessandro Lenci |
| Abstract | This paper empirically evaluates the performances of different state-of-the-art distributional models in a nominal lexical semantic classification task. We consider models that exploit various types of distributional features, which thereby provide different representations of nominal behavior in context. The experiments presented in this work demonstrate the advantages and disadvantages of each model considered. This analysis also considers a combined strategy that we found to be capable of leveraging the bottlenecks of each model, especially when large robust data is not available. |
| Topics | Lexicon, Lexical Database, Statistical and Machine Learning Methods |
Full paper  |
Choosing which to Use? A Study of Distributional Models for Nominal Lexical Semantic Classification |
| Bibtex | @InProceedings{ROMEO14.583, author = {Lauren Romeo and Gianluca Lebani and Núria Bel and Alessandro Lenci}, title = {Choosing which to Use? A Study of Distributional Models for Nominal Lexical Semantic Classification}, 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} } |