Marco Ernandes

SoCS Conference 2012 Conference Paper

Efficient Single Frontier Bidirectional Search

• Marco Lippi 0001
• Marco Ernandes
• Ariel Felner

The Single Frontier Bi-Directional Search (SBS) framework was recently introduced. A node in SBS corresponds to a pair of states, one from each of the frontiers and it uses front-to- front heuristics. In this paper we present an enhanced version of SBS, called eSBS, where pruning and caching techniques are applied, which significantly reduce both time and memory needs of SBS. We then present a hybrid of eSBS and IDA∗ which potentially uses only the square root of the memory required by A∗ but enables to prune many nodes that IDA∗ would generate. Experimental results show the benefit of our new approaches on a number of domains.

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).

AAAI Conference 2005 Conference Paper

WebCrow: A Web-Based System for Crossword Solving

• Marco Ernandes

Language games represent one of the most fascinating challenges of research in artificial intelligence. In this paper we give an overview of WebCrow, a system that tackles crosswords using the Web as a knowledge base. This appears to be a novel approach with respect to the available literature. It is also the first solver for non- English crosswords and it has been designed to be potentially multilingual. Although WebCrow has been implemented only in a preliminary version, it already displays very interesting results reaching the performance of a human beginner: crosswords that are “easy” for expert humans are solved, within competition time limits, with 80% of correct words and over 90% of correct letters.

ECAI Conference 2004 Conference Paper

Likely-Admissible and Sub-Symbolic Heuristics

• Marco Ernandes
• Marco Gori