Francesco Colace

Ontology for E-Learning: A Bayesian Approach

In the last decade, the evolution of educational technologies has forced an extraordinary interest in new methods for delivering learning content to learners. Today, distance education represents an effective way for supporting and sometimes substituting the traditional formative processes, thanks to the technological improvements achieved in the field in recent years. However, the role of technology has often been overestimated. The amount of information students can obtain from the Internet is huge, and as a result, they can easily be confused. Teachers can also be disconcerted by this vast quantity of content and are often unable to suggest the correct content to their students. In the open scientific literature, it is widely recognized that an important factor for success in delivering learning content is related to the capability for customizing the learning process for the specific needs of a given learner. This task is still far from having been fully accomplished, and there is a real interest in investigating new approaches and tools to adapt the formative process to specific individual needs. In this scenario, the introduction of ontology formalism can improve the quality of the formative process, allowing the introduction of new and effective services. Ontologies can lead to important improvements in the definition of a courses knowledge domain, in the generation of an adapted learning path, and in the assessment phase. This paper provides an initial discussion of the role of ontologies in the context of e-learning. The improvements related to the introduction of ontologies formalism in the e-learning field are discussed, and a novel algorithm for ontology building through the use of Bayesian networks is shown. Finally, the application of this algorithm in the assessment process and some experimental results are illustrated.

Text classification using a few labeled examples

Supervised text classifiers need to learn from many labeled examples to achieve a high accuracy. However, in a real context, sufficient labeled examples are not always available because human labeling is enormously time-consuming. For this reason, there has been recent interest in methods that are capable of obtaining a high accuracy when the size of the training set is small. In this paper we introduce a new single label text classification method that performs better than baseline methods when the number of labeled examples is small. Differently from most of the existing methods that usually make use of a vector of features composed of weighted words, the proposed approach uses a structured vector of features, composed of weighted pairs of words. The proposed vector of features is automatically learned, given a set of documents, using a global method for term extraction based on the Latent Dirichlet Allocation implemented as the Probabilistic Topic Model. Experiments performed using a small percentage of the original training set (about 1%) confirmed our theories.

Weighted Word Pairs for query expansion

Abstract This paper proposes a novel query expansion method to improve accuracy of text retrieval systems. Our method makes use of a minimal relevance feedback to expand the initial query with a structured representation composed of weighted pairs of words. Such a structure is obtained from the relevance feedback through a method for pairs of words selection based on the Probabilistic Topic Model. We compared our method with other baseline query expansion schemes and methods. Evaluations performed on TREC-8 demonstrated the effectiveness of the proposed method with respect to the baseline.

Terminological ontology learning and population using latent Dirichlet allocation

The success of Semantic Web will heavily rely on the availability of formal ontologies to structure machine understanding data. However, there is still a lack of general methodologies for ontology automatic learning and population, i.e. the generation of domain ontologies from various kinds of resources by applying natural language processing and machine learning techniques In this paper, the authors present an ontology learning and population system that combines both statistical and semantic methodologies. Several experiments have been carried out, demonstrating the effectiveness of the proposed approach. HighlightsA graph of terms can be effectively used for ontology building.Such a graph is extracted from documents thanks to a LDA based methodology.Ontology learning involves the use of annotated lexicons (WordNet).Proposed method achieves good performances on standard datasets.

A Probabilistic Framework for TV-News Stories Detection and Classification

In this paper we face the problem of partitioning the news videos into stories, and of their classification according to a predefined set of categories. In particular, we propose to employ a multi-level probabilistic framework based on the hidden Markov models and the Bayesian networks paradigms for the segmentation and the classification phases, respectively. The whole analysis is carried out exploiting information extracted from the video and the audio tracks using techniques of superimposed text recognition, speaker identification, speech transcription, anchor detection. The system was tested on a database of Italian news videos and the results are very promising

Work in progress - virtual lab for electronic engineering curricula

During last years the interest on distance learning techniques has grown steadily as far as the use of electronic instruments in experimentation is concerned. Due to the higher and higher number of students accessing the university educational structures, the cost of laboratories for didactical electronic applications is going to be very high. As a consequence, a number of software tools and environments have been developed to help users to share distributed laboratory resources and realize virtual experiments. Nevertheless, further solutions have to be explored when students must be trained and experienced in the instrumentation programming. In this paper, we exploit modern software technologies to design and implement a distributed architecture for virtual labs allowing the approach previously described. Services integrated in this architecture aim to support students both to keep contact with real instruments both to remotely program instrumentation. This distance learning methodology is discussed and some reports from students experience with the system are showed.

A Probabilistic Approach to Tweets' Sentiment Classification

Prior to 2003, mankind generated a total of about 5 Exabytes of contents. Now, we generate this amount of contents in about two days! The spread of generic (as Twitter, Facebook or Google+) or specialized (as Linked In or Viadeo) social networks allows sharing opinions on different aspects of life every day. Therefore this information is a rich source of data for opinion mining and sentiment analysis. This paper introduces a novel approach to the sentiment analysis based on the Weighted Word Pairs obtained by the use of the Latent Dirichlet Allocation (LDA) approach. The proposed methodology aims at identifying a word-based graphical model for depicting and mining a positive or negative attitude towards a topic. For the evaluation of the proposed approach a challenging scenario has been set: the real-time analysis of tweets. The experimental evaluation shows how the proposed approach is effective and satisfactory.

Adaptive hypermedia system in education: A user model and tracking strategy proposal

New technologies are quickly changing the traditional educational approaches and systems. In fact the integration of new technologies in the field of education offers new challenges and opportunities in distance learning, lifelong learning and e-learning in general. On the other hand e-Learning is an interesting application area for adaptive hypermedia system (AEH). Through the use of user models intelligent tutoring and students tracking techniques an AEH can recognize individual users and their needs adapting in an effective way the student learning path. This paper attempts to enhance student learning by addressing different learning styles by the use of the Adaptive Hypermedia System approach. In particular it addresses to define a standardized and simple user model and some tracking parameters and techniques in order to update the proposed learning path. The proposed system collects information on the user learning style by the use of various well known in literature test We propose a mapping of this acquired information in some parameters defined in standard metadata user description like IMS LIP. The final goal is a student learning style representation by the use of a quantitative model So we can develop a tracking strategy through the observation of the main model parameters. A similar approach could be used for the description of learning objects in order to provide the introduction of well defined metrics for the dynamic tailoring of the learning path to the students learning style and needs.

An Adaptive Contextual Recommender System: a Slow Intelligence Perspective

This paper introduces an Adaptive Context Aware Recommender system based on the Slow Intelligence approach. The system is made available to the user as an adaptive mobile application, which allows a high degree of customization in recommending services and resources according to his/her current position and global profile. A case study applied to the town of Pittsburgh has been analyzed considering various users (with different profiles as visitors, students, professors) and an experimental campaign has been conducted obtaining interesting results.

Text Classification Using a Graph of Terms

It is well known that supervised text classification methods need to learn from many labeled examples to achieve a high accuracy. However, in a real context, sufficient labeled examples are not always available. For this reason, there has been recent interest in methods that are capable of obtaining a high accuracy even if the size of the training set is not big. The main purpose of text mining techniques is to identify common patterns through the observation of vectors of features and then to use such patterns to make predictions. Most existing methods usually make use of a vector of features made up of weighted words that unfortunately are insufficiently discriminative when the number of features is much higher than the number of labeled examples. In this paper we demonstrate that, to obtain a greater accuracy in the analysis and revelation of common patterns, we could employ more complex features than simple weighted words. The proposed vector of features considers a hierarchical structure, named a mixed Graph of Terms, composed of a directed and an undirected sub-graph of words, that can be automatically constructed from a set of documents through the probabilistic Topic Model. The method has been tested on the top 10 classes of the ModApte split from the Reuters-21578 dataset, learned on several subsets of the original training set and showing a better performance than a method using a list of weighted words as a vector of features and linear support vector machines.