KeLP grew significantly in the past two years, from a laboratory project to a Maven Project containing more than 20,000 lines of code.
We (the KeLP team) believe that the old site is “too small” for KeLP and a better description of the available kernel methods and learning algorithms is required.
This new site provides larger documentations, new tutorial and examples, in a more structured form.
A new site is available at
… it provides larger documentations, new tutorial and examples, in a more structured form.
Please, let us know your opinion about the site and/or questions about KeLP!
LU4R is the adaptive spoken Language Understanding chain For(4) Robots tool, that is the result of the collaboration between the Semantic Analytics Group at the University of Roma, Tor Vergata, and the Laboratory of Cognitive Cooperating Robots (Lab.Ro.Co.Co.) at Sapienza, University of Rome.
LU4R receives as input one or more transcriptions of a spoken command for a robots and produces one or more linguistic predicates reflecting the actions intended by the user. Predicates, as well as their arguments, are consistent with a linguistically-motivated representation and coherent with the environment perceived by the robot. The interpretation process is sensitive to different configurations of the environment (possibly synthesized through a Semantic Map or different approaches) that collect all the information about the entities populating the operating world. The tool is fully implemented in Java and is released according to a Client/Server architecture, in order to decouple the chain from the specific robotic platform that will use it.
You can find more information about LU4R and download it at: http://sag.art.uniroma2.it/lu4r.html
We are proud to announce that the paper Context–aware Spoken Language Understanding for Human Robot Interaction by Andrea Vanzo, Danilo Croce, Roberto Basili and Daniele Nardi has been awarded with the Best Young Paper Award at the CLIC-it 2016 conference!
The paper presented LU4R, the adaptive spoken Language Understanding chain For(4) Robots tool. More details about LU4R can be found here.
A tutorial showing how to implement with KeLP a Kernel-based classifier for the Question Classification task has been presented at the Web Mining and Retrieval course. The tutorial can be downloaded from the course WmIR course webpage.
You can find the slides at this link and the tutorial material at this link.
The Kelp group, mainly composed of people from the SAG laboratories (Simone Filice, Danilo Croce and Roberto Basili) ranked first at the subtask A within the SemEval 2016 Task 3: Community Question Answering!
Moreover the Kelp group ranked third and second with respect to the Subtask B and Subtask C, respectively (among up to 12 groups).
The list of participants and the detailed results can be downloaded at this link.
The paper “Large-scale Kernel-based Language Learning through the Ensemble Nystrom methods” by Danilo Croce and Roberto Basili has been accepted at the 8th European Conference on Information Retrieval (ECIR 2016) that will be held on 20-23 March 2016 in Padua, Italy (acceptance rate: 21%)
The list of accepted paper can be browsed at this link.
Abstract: Kernel methods have been used by many Machine Learning paradigms, achieving state-of-the-art performances in many Language Learning tasks. One drawback of expressive kernel functions, such as Sequence or Tree kernels, is the time and space complexity required both in learning and classification. In this paper, the Nystrom methodology is studied as a viable solution to face these scalability issues.
By mapping data in low-dimensional spaces as kernel space approximations, the proposed methodology positively impacts on scalability through compact linear representation of highly structured data. Computation can be also distributed on several machines by adopting the so-called Ensemble Nystrom Method.
Experimental results show that an accuracy comparable with state-of-the-art kernel-based methods can be obtained by reducing of orders of magnitude the required operations and enabling the adoption of datasets containing more than one million examples.
The Context-based Corpus for Sentiment Analysis in Twitter used in the COLING 2014 best paper
Andrea Vanzo, Danilo Croce, Roberto Basili (2014): A context based model for Sentiment Analysis in Twitter. In: Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics, pp. 2345–2354, Dublin City University and Association for Computational Linguistics, Dublin, Ireland, 2014.
has been released.
Please find further details at:
KeLP (Kernel-based Learning Platform) is a machine learning platform developed within the SAG group. It is entirely written in Java and it is strongly focused on Kernel Machines.
For a deeper look, you can visit this page.
The paper “A Stratified Strategy for Efficient Kernel-based Language Learning” from Filice, Croce and Basili has been accepted at the Twenty-Ninth Conference on Artificial Intelligence (AAAI 2015).