Leonardo Rigutini

ECAI Conference 2010 Conference Paper

Multitask Kernel-based Learning with Logic Constraints

• Michelangelo Diligenti
• Marco Gori
• Marco Maggini
• Leonardo Rigutini

This paper presents a general framework to integrate prior knowledge in the form of logic constraints among a set of task functions into kernel machines. The logic propositions provide a partial representation of the environment, in which the learner operates, that is exploited by the learning algorithm together with the information available in the supervised examples. In particular, we consider a multi-task learning scheme, where multiple unary predicates on the feature space are to be learned by kernel machines and a higher level abstract representation consists of logic clauses on these predicates, known to hold for any input. A general approach is presented to convert the logic clauses into a continuous implementation, that processes the outputs computed by the kernel-based predicates. The learning task is formulated as a primal optimization problem of a loss function that combines a term measuring the fitting of the supervised examples, a regularization term, and a penalty term that enforces the constraints on both supervised and unsupervised examples. The proposed semi-supervised learning framework is particularly suited for learning in high dimensionality feature spaces, where the supervised training examples tend to be sparse and generalization difficult. Unlike for standard kernel machines, the cost function to optimize is not generally guaranteed to be convex. However, the experimental results show that it is still possible to find good solutions using a two stage learning schema, in which first the supervised examples are learned until convergence and then the logic constraints are forced. Some promising experimental results on artificial multi-task learning tasks are reported, showing how the classification accuracy can be effectively improved by exploiting the a priori rules and the unsupervised examples.

IJCAI Conference 2007 Conference Paper

• Marco Ernandes
• Giovanni Angelini
• Marco Gori
• Leonardo Rigutini
• franco scarselli

Term weighting systems are of crucial importance in Information Extraction and Information Retrieval applications. Common approaches to term weighting are based either on statistical or on natural language analysis. In this paper, we present a new algorithm that capitalizes from the advantages of both the strategies by adopting a machine learning approach. In the proposed method, the weights are computed by a parametric function, called Context Function, that models the semantic influence exercised amongst the terms of the same context. The Context Function is learned from examples, allowing the use of statistical and linguistic information at the same time. The novel algorithm was successfully tested on crossword clues, which represent a case of Single-Word Question Answering.

ECAI Conference 2006 Conference Paper

Adaptive Context-Based Term (Re)Weighting: An Experiment on Single-Word Question Answering

• Marco Ernandes
• Giovanni Angelini
• Marco Gori
• Leonardo Rigutini
• Franco Scarselli

Term weighting is a crucial task in many Information Retrieval applications. Common approaches are based either on statistical or on natural language analysis. In this paper, we present a new algorithm that capitalizes from the advantages of both the strategies. In the proposed method, the weights are computed by a parametric function, called Context Function, that models the semantic influence exercised amongst the terms. The Context Function is learned by examples, so that its implementation is mostly automatic. The algorithm was successfully tested on a data set of crossword clues, which represent a case of Single-Word Question Answering.

ECAI Conference 2006 Conference Paper

Automatic Term Categorization by Extracting Knowledge from the Web

• Leonardo Rigutini
• Ernesto Di Iorio
• Marco Ernandes
• Marco Maggini

This paper addresses the problem of categorizing terms or lexical entities into a predefined set of semantic domains exploiting the knowledge available on-line in the Web. The proposed system can be effectively used for the automatic expansion of thesauri, limiting the human effort to the preparation of a small training set of tagged entities. The classification of terms is performed by modeling the contexts in which terms from the same class usually appear. The Web is exploited as a significant repository of contexts that are extracted by querying one or more search engines. In particular, it is shown how the required knowledge can be obtained directly from the snippets returned by the search engines without the overhead of document downloads. Since the Web is continuously updated “World Wide”, this approach allows us to face the problem of open-domain term categorization handling both the geographical and temporal variability of term semantics. The performances attained by different text classifiers are compared, showing that the accuracy results are very good independently of the specific model, thus validating the idea of using term contexts extracted from search engine snippets. Moreover, the experimental results indicate that only very few training examples are needed to reach the best performance (over 90% for the F1 measure).