A software suite for SUPervised Word Sense Disambiguation
SupWSD Toolkit is an easy-to-use tool for the research community, designed to be modular, fast and scalable for training and testing on large datasets. Our framework includes the implementation of a state-of-the-art supervised WSD system together with a NLP pipeline.Get Start
SupWSD API is a web service that gives you programmatic access to SupWSD. The service is available for English, French, German, Italian and Spanish and support 5 different trained models (SemCor, OMSTI, Train-O-Matic, OneSec & WordNet Glosses).Get Start
SupWSD Pocket is a light version of SupWSD which allows you to perform the disambiguation process in offline mode using the best-known configuration, without the need to configure the toolkit pipeline. Language models are available for download.Get Start
- State-of-the-art accuracy
- Low memory requirement
- Optimized for larger datasets
- Designed to be modular, extendable and scalables
- 6 different parser types
- Supports the most widely used NLP pipeline
- Pretrained word embeddings
- Support for Wordnet and Babelnet sense inventory
Up to 6X FASTER than other systemsless memory consumption
We relied on a testing corpus with 1M words and more than 250K target instances to disambiguate, and we used both frameworks on SemCor and OMSTI as training sets. Results show a considerable gain in execution time achieved by SupWSD, which is around 3 times faster on Semcor, and almost 6 times faster on OMSTI than other systems.
Furthermore, our system parallelize the preprocessing module’s execution and implements lazy loading techniques to make it less memory-intensive on large datasets.
We evaluated SupWSD on the evaluation framework of Raganato et al. (2017), which includes 5 test sets from the Senseval/Semeval series and two training corpus of different size, i.e. SemCor (Miller et al., 1993) and OMSTI (Taghipourand Ng, 2015). Our system guarantees the state-of-the-art performance in terms of F-Measure, sometimes even outperforming its competitor by a considerable margin.
|System||Senseval 2||Senseval 3||SemEval 7||Semeval 13||Semeval 15|
SupWSD meets Neural Networks!!
Our goal is to build a new classifier capable of covering the entire sense inventory, using the definitions of the senses as annotations.
Modular Toolkit GUI
Are you a student, a teacher or a researcher?
You will like to know that we are building a new toolkit with a graphical user interface to simplify model training and testing and provide new interesting features.