Davide Colla, Enrico Mensa and Daniele P. Radicioni. Novel metrics for computing semantic similarity with sense embeddings, Knowledge-based systems
, vol. 206, 2020.
In the last years many efforts have been spent to build word embeddings, a representational device in which word meanings are described through dense unit vectors of real numbers over a continuous, high-dimensional Euclidean space, where similarity can be interpreted as a metric. Afterwards, sense-level embeddings have been proposed to describe the meaning of senses, rather than terms. More recently, additional intermediate representations have been designed, providing a vector description for pairs (term,sense), and mapping both term and sense descriptions onto a shared semantic space. However, surprisingly enough, this wealth of approaches and resources has not been supported by a parallel refinement in the metrics used to compute semantic similarity: to date, the semantic similarity featuring two input entities is mostly computed as the maximization of some angular distance intervening between vector pairs, typically cosine similarity. In this work we introduce two novel similarity metrics to compare sense-level representations, and show that by exploiting the features of sense-embeddings it is possible to substantially improve on existing strategies, by obtaining enhanced correlation with human similarity ratings. Additionally, we argue that semantic similarity needs to be complemented by another task, involving the identification of the senses at the base of the similarity rating. We experimentally verified that the proposed metrics are beneficial when dealing with both semantic similarity task and sense identification task. The experimentation also provides a detailed how-to illustrating how six important sets of sense embeddings can be used to implement the proposed similarity metrics.
Davide Colla, Enrico Mensa and Daniele P. Radicioni. LESSLEX: Linking multilingual Embeddings to SenSe representations of Lexical items Computational Linguistics
, 46(2), pages 289-333, June 2020.
We present LessLex, a novel multilingual lexical resource. Different from the vast majority of existing approaches, we ground our embeddings on a sense inventory made available from the BabelNet semantic network. In this setting, multilingual access is governed by the mapping of terms onto their underlying sense descriptions, such that all vectors co-exist in the same semantic space. As a result, for each term we have thus the 'blended' terminological vector along with those describing all senses associated to that term. LessLex has been tested on three tasks relevant to lexical semantics: conceptual similarity, contextual similarity, and semantic text similarity: we experimented over the principal data sets for such tasks in their multilingual and cross-lingual variants, improving on or closely approaching state-of-the-art results. We conclude by arguing that LessLex vectors may be relevant for practical applications and for research on conceptual and lexical access and competence.
Francesca Garbarini, Fabrizio Calzavarini, Matteo Diano, Monica Biggio, Carola Barbero, Daniele P Radicioni, Giuliano Geminiani, Katiuscia Sacco, and Diego Marconi. Imageability effect on the functional brain activity during a naming to definition task. Neuropsychologia
, 137:107275, 2020.
Lexical competence includes both the ability to relate words to the external world as accessed through (mainly) visual perception (referential competence) and the ability to relate words to other words (inferential competence). We investigated the role of visual imagery in lexical inferential competence by using an auditory version of an inferential naming-to-definition task, in which visual imageability of both definitions and target words was manipulated. A visual imageability-related brain activity (bilateral posterior-parietal lobe and ventrotemporal cortex, including fusiform gyrus) was found during a “pure” inferential performance. The definition effect in high vs. low imageability contrast suggests that a visual-imagery strategy is spontaneously activated during the retrieval of a word from a high imageable definition; such an effect appears to be independent of whether the target word is high or low imageable. This contributes to the understanding of the neural correlates of semantic processing and the differential role of spontaneous visual imagery, depending on the semantic properties of the processed stimuli.
Valerio Basile, Tommaso Caselli, and Daniele P. Radicioni. Meaning in Con- text: Ontologically and linguistically motivated representations of objects and events. Applied Ontology
, 14:335-341, 2019.
Annamaria Goy, Cristina Accornero, Dunia Astrologo, Davide Colla, Matteo D'Ambrosio, Rossana Damiano, Marco Leontino, Antonio Lieto, Fabrizio Loreto, Diego Magro, Enrico Mensa, Alice Montanaro, Valeria Mosca, Stefano Musso, Daniele P. Radicioni, and Cristina Re. Fruitful synergies between computer science, historical studies and archives: The experience in the PRiSMHA project. In Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2019
, Volume 3: KMIS, Vienna, Austria, September 17-19, 2019, pages 225-230, 2019.
In this paper we present the mid-term results of the PRiSMHA project, aimed to contribute in building a digital "smart archivist", i.e., a web-based system providing an innovative access to historical archives. Such a system is endowed with a semantic layer over existing archival metadata, including computational ontologies and a knowledge base, containing a formal description of the content of the documents stored in the archives. The paper focuses on the fruitful synergies employed to reach its goal. In particular, it explains the steps of the "spiral" process implemented for creating a full-fledged formal semantic model, through the interaction between computer scientists, historians, and archivists. The paper also presents some "core side- effects" of this process: an analytical card for each document has been produced, all selected documents have been digitized, OCR-ized (when possible), and linked to a record on the archival platform. This experience enabled us to define a virtuous procedural model, from the paper documents up to the digital "smart archivist", based on a close collaboration between universities and cultural and historical institutions.
Davide Colla, Marco Leontino, Enrico Mensa, and Daniele P. Radicioni. From Sartre to Frege in three steps: A* search for enriching semantic text similarity measures. In Proceedings of the Sixth Italian Conference on Computational Linguistics
, Bari, Italy, November 13-15, 2019, 2019.
In this paper we illustrate a preliminary investigation on semantic text similarity. In particular, the proposed approach is aimed at complementing and enriching the categorization results obtained by employing standard distributional resources. We found that the paths connecting entities and concepts from documents at stake provide interesting information on the connections between document pairs. Such semantic browsing device enables further semantic processing, aimed at unveiling contexts and hidden connections (possibly not explicitly mentioned in the documents) between text documents.
Aureliano Porporato, Alessandro Mazzei, Daniele P. Radicioni, and Rosa Meo. Evaluating the mume dialogue system with the IDIAL protocol. In Proceedings of the Sixth Italian Conference on Computational Linguistics
, Bari, Italy, November 13-15, 2019.
In this paper we describe the implementation of the MuMe dialogue system, a task-based dialogue system for a car sharing service, and its evaluation through the IDIAL protocol. Finally we report some comments on this novel dialogue system evaluation method.
Giulio Carducci, Marco Leontino, Daniele P. Radicioni, Guido Bonino, Enrico Pasini, and Paolo Tripodi. Semantically aware text categorisation for metadata annotation. In Paolo Manghi, Leonardo Candela, and Gianmaria Silvello, editors, Digital Libraries: Supporting Open Science
, pages 315-330, Cham, 2019. Springer International Publishing.
In this paper we illustrate a system aimed at solving a long-standing and challenging problem: acquiring a classifier to automatically annotate bibliographic records by starting from a huge set of unbalanced and unlabelled data. We illustrate the main features of the dataset, the learning algorithm adopted, and how it was used to discriminate philosophical documents from documents of other disciplines. One strength of our approach lies in the novel combination of a standard learning approach with a semantic one: the results of the acquired classifier are improved by accessing a semantic network containing conceptual information. We illustrate the experimentation by describing the construction rationale of training and test set, we report and discuss the obtained results and conclude by drawing future work.
Enrico Mensa, Daniele P. Radicioni, and Antonio Lieto. COVER: a linguistic resource combining common sense and lexicographic information. Language Resources & Evaluation
, 52(4):921–948, 2018.
Lexical resources are fundamental to tackle many tasks that are central to present and prospective research in Text Mining, Information Retrieval, and connected to Natural Language Processing. In this article we introduce COVER, a novel lexical resource, along with COVERAGE, the algorithm devised to build it. In order to describe concepts, COVER proposes a compact vectorial representation that combines the lexicographic precision characterizing BabelNet and the rich common-sense knowledge featuring ConceptNet. We propose COVER as a reliable and mature resource, that has been employed in as diverse tasks as conceptual categorization, keywords extraction, and conceptual similarity. The experimental assessment is performed on the last task: we report and discuss the obtained results, pointing out future improvements. We conclude that COVER can be directly exploited to build applications, and coupled with existing resources, as well.
Enrico Mensa, Aureliano Porporato, and Daniele P. Radicioni. Annotating Concept Abstractness by Common-sense Knowledge. In Proceedings of the 17th International Conference of the Italian Association for Artificial Intelligence
, Lecture Notes in Artificial Intelligence (LNAI), Cham, 2018. Springer International Publishing.
Dealing with semantic representations of concepts involves collecting information on many aspects that collectively contribute to (lexical, semantic and ultimately) linguistic competence. In the last few years mounting experimental evidences have been gathered in the fields of Neuroscience and Cognitive Science on conceptual access and retrieval dynamics that posit novel issues, such as the imageability associated to terms and concepts, or abstractness features as a correlate of figurative uses of language. However, this body of research has not yet penetrated Computational Linguistics: specifically, as regards as Lexical Semantics, in the last few years the field has been dominated by distributional models and vectorial representations. We recently proposed COVER, that relies on a partly different approach. Conceptual descriptions herein are aimed at putting together the lexicographic precision of BabelNet and the common-sense available in ConceptNet. We now propose Abs-COVER, that extends the existing lexical resource by associating an abstractness score to the concepts contained therein. We introduce the detailed algorithms and report about an extensive evaluation on the renewed resource, where we obtained correlations with human judgements in line or higher compared to state of the art approaches.
Davide Colla, Enrico Mensa, Aureliano Porporato, and Daniele P. Radicioni. Conceptual Abstractness: from Nouns to Verbs. In Proceedings of the Fifth Italian Conference on Computational Linguistics (CLIC-IT 2018)
Investigating lexical access, representation and processing involves dealing with conceptual abstractness: abstract concepts are known to be more quickly and easily delivered in human communications than abstract meanings (Binder et al., 2005). Although these aspects have long been left unexplored, they are relevant: abstract terms are widespread in ordinary language, as they contribute to the realization of various sorts of figurative language (metaphors, metonymies, hyperboles, etc.). Abstractness is therefore an issue for computational linguistics, as well. In this paper we illustrate how to characterize verbs with abstractness information. We provide an experimental evaluation of the presented approach on the largest existing corpus annotated with abstraction scores: our results exhibit good correlation with human ratings, and point out some open issues that will be addressed in future work.
Davide Colla, Enrico Mensa, Daniele P. Radicioni, and Antonio Lieto. Tell me why: Computational explanation of conceptual similarity judgments. In J. Medina et al., editor, Proceedings of the 17th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU), Special Session on Advances on Explainable Artificial Intelligence
, volume 853 of Communications in Computer and Information Science (CCIS), pages 74–85, Cham, 2018. Springer International Publishing.
In this paper we introduce a system for the computation of explanations that accompany scores in the conceptual similarity task. In this setting the problem is, given a pair of concepts, to provide a score that expresses in how far the two concepts are similar. In order to explain how explanations are automatically built, we illustrate some basic features of COVER, the lexical resource that underlies our approach, and the main traits of the MERALI system, that computes conceptual similarity and explanations, all in one. To assess the computed explanations, we have designed a human experimentation, that provided interesting and encouraging results, which we report and discuss in depth.
Enrico Mensa, Aureliano Porporato, and Daniele P. Radicioni. Grasping metaphors: Lexical semantics in metaphor analysis. In Proceedings of the 15th Extended Semantic Web Conference (ESWC2018)
, Lecture Notes in Computer Science (LNCS), Cham, 2018. Springer International Publishing.
Metaphors represent to date an extraordinary challenge for computational linguistics. Dealing with metaphors has relevant consequences on our ability to build agents and systems that understand Natural Language and text documents: annotating metaphoric constructions by linking the metaphor elements to existing resources is a crucial step to make text documents more easily accessible by machines. Our approach tackles metaphors by considering concepts and their abstractness. We report the encouraging results obtained in a preliminary experimentation; we elaborate on present limitations, and individuate the needed improvements, which will be at base of future work.
Davide Colla, Enrico Mensa, and Daniele P. Radicioni. Semantic measures for keywords extraction. In Floriana Esposito, Roberto Basili, Stefano Ferilli, and Francesca A. Lisi, editors, AI*IA 2017 Advances in Artificial Intelligence: XVIth International Conference of the Italian Association for Artificial Intelligence
, Lecture Notes in Artificial Intelligence, pages 128-140, Cham, 2017. Springer International Publishing.
In this paper we introduce a minimalist hypothesis for keywords extraction: keywords can be extracted from text documents by considering concepts underlying document terms. Furthermore, central concepts are individuated as the concepts that are more related to title concepts. Namely, we propose five metrics, that are diverse in essence, to compute the centrality of concepts in the document body with respect to those in the title. We finally report about an experimentation over a popular data set of human annotated news articles; the results confirm the soundness of our hypothesis.
Vincenzo Lombardo, Fabrizio Piana, Dario Mimmo, Enrico Mensa, and Daniele P. Radicioni. Semantic models for the geological mapping process. In Floriana Esposito, Roberto Basili, Stefano Ferilli, and Francesca A. Lisi, editors, AI*IA 2017 Advances in Artificial Intelligence: XVIth International Conference of the Italian Association for Artificial Intelligence
, Lecture Notes in Artificial Intelligence, pages 295-306, Cham, 2017. Springer International Publishing.
The geologic mapping process requires the organization of data
according tothe general knowledge about the objects in the map,
namely the geologic units, and tothe objectives of a graphic
representation of such objects in a map, following some established
model of geotectonic evolution. Semantics can greatly help such a
process in providing a terminological base to name and classify the
objects of the map and supporting the application of reasoning
mechanisms for the derivation of novel properties and relations about
the objects of the map.
Antonio Lieto, Daniele P. Radicioni, Valentina Rho, and Enrico Mensa. Towards a Unifying Framework for Conceptual Represention and Reasoning in Cognitive Systems. Intelligenza Artificiale
, 11(2):139-153, 2017.
In this paper we present the rationale adopted for the integration of the knowledge level of Dual PECCS, a cognitive system for conceptual representation and categorization, with two different cognitive architectures: SOAR and LIDA. In previous works we already showed how the representational and reasoning framework adopted in Dual PECCS was integrable with diverse cognitive architectures, i.e. ACT-R and CLARION, making different representational assumptions and adopting diverse knowledge processing mechanisms.
The additional integrations presented here suggest that the underlying knowledge representation and reasoning structure adopted in Dual PECCS can be used as a unifying framework for the knowledge level of agents endowed with different cognitive architectures.
The current version of the system has been experimentally assessed in a task of conceptual categorization where a target concept illustrated by a simple common-sense linguistic description had to be identified by resorting to a mix of categorization strategies. The output has then been compared to human and artificial responses. The novel integration allowed us to extend our previous evaluation.
Enrico Mensa, Daniele P. Radicioni, and Antonio Lieto. MeRaLi at Semeval-2017 task 2 subtask 1: a cognitively inspired approach. In Proceedings of the International Workshop on Semantic Evaluation (SemEval 2017)
. Association for Computational Linguistics, 2017.
In this paper we report on the participation of the MeRaLi system to the SemEval Task 2 Subtask 1. The MeRaLi system approaches conceptual similarity through a simple, cognitively inspired, heuristics; it builds on a linguistic resource, the TTCSe, that relies on BabelNet, NASARI and ConceptNet. The linguistic resource in fact contains a novel mixture of common-sense and encyclopedic knowledge. The obtained results point out that there is ample room for improvement, so that they are used to elaborate on present limitations and on future steps.
Enrico Mensa, Daniele P. Radicioni, and Antonio Lieto. TTCSe: a Vectorial Resource for Computing Conceptual Similarity. In Proceedings of the 1st Workshop on Sense, Concept and Entity Representations and their Applications - EACL 2017
, pages 96–101, Valencia, Spain, April 2017. Association for Computational Linguistics.
In this paper we introduce the TTCSe, a linguistic resource that relies on BabelNet, NASARI and ConceptNet, that has now been used to compute the conceptual similarity between concept pairs. The conceptual representation herein provides uniform access to concepts based on BabelNet synset IDs, and consists of a vector-based semantic representation which is compliant with the Conceptual Spaces, a geometric framework for common-sense knowledge representation and reasoning. The TTCSe has been evaluated in a preliminary experimentation on a conceptual similarity task.
Anna Goy, Rossana Damiano, Fabrizio Loreto, Diego Magro, Stefano Musso, Daniele P. Radicioni, Cristina Accornero, Davide Colla, Antonio Lieto, Enrico Mensa, Marco Rovera, Dunia Astrologo, Bruno Boniolo, and Matteo D'Ambrosio. PRiSMHA (Providing Rich Semantic Metadata for Historical Archives). In Stefano Borgo, Oliver Kutz, Frank Loebe, and Fabian Neuhaus, editors, Proceedings of the Joint Ontology Workshops 2017 Episode 3: The Tyrolean Autumn of Ontology
, volume 2050. CEUR, 2017.
In this paper we present the PRiSMHA project, whose main goal is to demonstrate that a rich semantic representation of the content of historical documents is useful - since it can significantly improve the access to archival resources - and sustainable - thanks to a crowdsourcing approach. This goal poses interesting research challenges, both for the semantic model definition and the user interaction. Such challenges range from the dialog between computer scientists and historians, to the design of an effective ontology-driven user interface; from the strategies to ensure the quality of the semantic metadata produced, to the application of Information Extraction techniques to support user annotation.
Antonio Lieto, Daniele P. Radicioni, and Valentina Rho. Dual PECCS: a cognitive system for conceptual representation and categorization. Journal of Experimental & Theoretical Artificial Intelligence
, pages 1–20, 2016.
In this article we present an advanced version of Dual-PECCS, a cognitively-inspired knowledge representation and reasoning system aimed at extending the capabilities of artificial systems in conceptual categorization tasks. It combines different sorts of common-sense categorization (prototypical and exemplars-based categorization) with standard monotonic categorization procedures. These different types of inferential procedures are reconciled according to the tenets coming from the dual process theory of reasoning. On the other hand, from a representational perspective, the system relies on the hypothesis of conceptual structures represented as heterogeneous proxytypes. Dual-PECCS has been experimentally assessed in a task of conceptual categorization where a target concept illustrated by a simple common-sense linguistic description had to be identified by resorting to a mix of categorization strategies, and its output has been compared to human responses. The obtained results suggest that our approach can be beneficial to improve the representational and reasoning conceptual capabilities of standard cognitive artificial systems, and –in addition– that it may be plausibly applied to different general computational models of cognition. The current version of the sys tem, in fact, extends our previous work, in that Dual-PECCS is now integrated and tested into two cognitive architectures, ACT-R and CLARION, implementing different assumptions on the underlying invariant structures governing human cognition. Such integration allowed us to extend our previous evaluation.
Antonio Lieto and Daniele P. Radicioni. From human to artificial cognition and back: New perspectives on cognitively inspired AI systems. Cognitive Systems Research
, 39:1–3, 2016.
We overview the main historical and technological elements characterising the rise, the fall and the recent renaissance of the cognitive approaches to Artificial Intelligence and provide some insights and suggestions about the future directions and challenges that, in our opinion, this discipline needs to face in the next years.
Antonio Lieto, Enrico Mensa and Daniele P. Radicioni. A Resource-Driven Approach for Anchoring Linguistic Resources to Conceptual Spaces. In XVth International Conference of the Italian Association for Artificial Intelligence
, Genova, Italy, 2016.
In this paper we introduce the TTCS system, so named after Terms To Conceptual Spaces, that exploits a resource-driven approach relying on BabelNet, NASARI and ConceptNet. TTCS takes in input a term and its context of usage and produces as output a specific type of vector-based semantic representation, where conceptual information is encoded through the Conceptual Spaces (a geometric framework for common-sense knowledge representation and reasoning). The system has been evaluated in a twofold experimentation. In the first case we assessed the quality of the extracted common-sense conceptual information with respect to human judgments with an online questionnaire. In the second one we compared the performances of a conceptual categorization system that was run twice, once fed with extracted annotations and once with hand-crafted annotations. In both cases the results are encouraging and provide precious insights to make substantial improvements.
Antonio Lieto, Enrico Mensa and Daniele P. Radicioni. Taming Sense Sparsity: a Common-Sense Approach. In Proceedings of Third Italian Conference on Computational Linguistics (CLiC-it 2016) & Fifth Evaluation Campaign of Natural Language Processing and Speech Tools for Italian
, volume 10037 of Lecture Notes in Artificial Intelligence
, pages 435–449. Springer, 2016.
We present a novel algorithm and a linguistic resource named CloSEST after 'Common SEnse STrainer'. The resource contains a list of the main senses associated to a given term, and it was obtained by applying a simple set of pruning heuristics to the senses provided in the NASARI vectors for the set of 15K most frequent English terms. The preliminary experimentation provided encouraging results.
Antonio Lieto, Daniele P. Radicioni and Valentina Rho. A Common-Sense Conceptual Categorization System Integrating Heterogeneous Proxytypes and the Dual Process of Reasoning. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI)
, Buenos Aires, AAAI Press, July 2015.
In this article we present DUAL-PECCS, an integrated Knowledge Representation system aimed at extending artificial capabilities in tasks such as conceptual categorization. It relies on two different sorts of cognitively inspired common-sense reasoning: prototypical reasoning and exemplars-based reasoning. Furthermore, it is grounded on the theoretical tenets coming from the dual process theory of the mind, and on the hypothesis of heterogeneous proxytypes, developed in the area of the biologically inspired cognitive architectures (BICA). The system has been integrated into the ACT-R cognitive architecture, and experimentally assessed in a conceptual categorization task, where a target concept illustrated by a simple common-sense linguistic description had to be identified by resorting to a mix of categorization strategies. Compared to human-level categorization, the obtained results suggest that our proposal can be helpful in extending the representational and reasoning conceptual capabilities of standard cognitive artificial systems.
Antonio Lieto, Daniele P. Radicioni, Marcello Frixione, and Valentina Rho. Extending Ontological Categorization Through a Dual Process Conceptual Architecture. In Ana Fred, Jan L. G. Dietz, David Aveiro, Kecheng Liu, and Joaquim Filipe, editors, Knowledge Discovery, Knowledge Engineering and Knowledge Management
, volume 553 of Communications in Computer and Information Science, pages 313-328. Springer International Publishing, 2015.
In this work we present a hybrid knowledge representation system aiming at extending the representational and reasoning capabilities of classical ontologies by taking into account the theories of typicality in conceptual processing. The system adopts a categorization process inspired to the dual process theories and, from a representational perspective, is equipped with a heterogeneous knowledge base that couples conceptual spaces and ontological formalisms. The system has been experimentally assessed in a conceptual categorization task where common sense linguistic descriptions were given in input, and the corresponding target concepts had to be identified. The results show that the proposed solution substantially improves the representational and reasoning "conceptual" capabilities of standard ontology-based systems.
Andrea Minieri, Antonio Lieto, Alberto Piana, and Daniele P. Radicioni. A knowledge-based system for prototypical reasoning. Connection Science
, 27(2):137–152, 2015.
In this work we present a knowledge-based system equipped with a hybrid, cognitively inspired architecture for the representation of conceptual information. The proposed system aims at extending the classical representational and reasoning capabilities of the ontology-based frameworks towards the realm of the prototype theory. It is based on a hybrid knowledge base, composed of a classical symbolic component (grounded on a formal ontology) with a typicality based one (grounded on the conceptual spaces frame- work). The resulting system attempts to reconcile the heterogeneous approach to the concepts in Cognitive Science with the dual process theories of reasoning and rationality. The system has been experimentally assessed in a conceptual categorization task where common sense linguistic descriptions were given in input, and the corresponding target concepts had to be identified. The results show that the proposed solution substantially extends the representational and reasoning ‘conceptual’ capabilities of standard ontology-based systems.
Edoardo Acotto and Daniele P. Radicioni. Sulla rilevanza delle emozioni musicali: un approccio computazionale. Sistemi Intelligenti
, 27(2):427–438, 2015.
Antonio Lieto, Daniele P. Radicioni, and Valentina Rho. Integrating a cognitive framework for knowledge representation and categorization in diverse cognitive architectures. In Olivier L. Georgeon, editor, BICA, volume 71 of Procedia Computer Science
, pages 92–98. Elsevier, 2015.
Daniele P. Radicioni, Francesca Garbarini, Fabrizio Calzavarini, Monica Biggio, Antonio Lieto, Katiuscia Sacco and Diego Marconi. On Mental Imagery in Lexical Processing: Computational Modeling of the Visual Load Associated to Concepts. In G. Airenti and M. Cruciani (Editors) Proceedings of the EuroAsianPacific Joint Conference on Cognitive Science (EAP-COGSCI 2015)
, 181-186, CEUR-WS.org, ISSN 1613-0073, 2015.
This paper investigates the notion of visual load, an estimate for a lexical item’s efficacy in activating mental images associated with the concept it refers to. We elaborate on the centrality of this notion which is deeply and variously connected to lexical processing. A computational model of the visual load is introduced that builds on few low level features and on the dependency structure of sentences. The system implementing the proposed model has been experimentally assessed and shown to reasonably approximate human response.
Antonio Lieto, Andrea Minieri, Alberto Piana, and Daniele P. Radicioni. A Dual Process Architecture for Ontology-Based Systems. In Joaquim Filipe, Jan Dietz, and David Aveiro, editors, Proceedings of KEOD 2014, 6th International Conference on Knowledge Engineering and Ontology Development
, pages 48–55. SCITEPRESS – Science and Technology Publications, 2014.
In this work we present an ontology-based system equipped with a hybrid architecture for the representation of conceptual information. The proposed system aims at extending the representational and reasoning capabilities of classical ontology-based systems towards more realistic and cognitively grounded scenarios, such as those envisioned by the prototype theory. The resulting system attempts to reconcile the heterogeneous approach to the concepts in Cognitive Science and the dual process theories of reasoning and rationality. The system has been experimentally assessed in a conceptual categorization task where common sense linguistic descriptions were given in input, and the corresponding target concepts had to be identified. The results show that the proposed solution substantially improves on the representational and reasoning "conceptual" capabilities of standard ontology-based systems.
R. Esposito, D. P. Radicioni, A. Visconti. CDoT: Optimizing MAP Queries on Trees. In M. Baldoni, C. Baroglio and G. Boella (Eds.), AI*IA 2013: 13th Conference of the Italian Association for Artificial Intelligence
, 481-492, LNAI 8249, 2013. Springer International Publishing Switzerland.
Among the graph structures underlying Probabilistic Graphical Models, trees are valuable tools for modeling several interesting problems, such as linguistic parsing, phylogenetic analysis, and music harmony analysis. In this paper we introduce CDoT, a novel exact algorithm for answering Maximum a Posteriori queries on tree structures. We discuss its properties and study its asymptotic complexity; we also provide an empirical assessment of its performances, showing that the proposed algorithm substantially improves over a dynamic programming based competitor.
L. Ghignone, A. Lieto, D. P. Radicioni. Typicality-Based Inference by Plugging Conceptual Spaces Into Ontologies. In Lieto, Cruciani (Eds.), Proceedings of the International Workshop on Artificial Intelligence and Cognition
, CEUR, 2013.
In this paper we present a cognitively inspired system for the representation of conceptual information in an ontology-based environment. It builds on the heterogeneous notion of concepts in Cognitive Science and on the so-called dual process theories of reasoning and rationality, and it provides a twofold view on the same artificial concept, combining a classical symbolic component (grounded on a formal ontology) with a typicality-based one (grounded on the conceptual spaces framework). The implemented system has been tested in a pilot experimentation regarding the classification task of linguistic stimuli. The results show that this modeling solution extends the representational and reasoning "conceptual" capabilities of standard ontology-based systems.
A. Mastropaolo, F. Pallante, and D. P. Radicioni. Legal Documents Categorization by Compression. In B. Verheij, editor, Proceedings of ICAIL 2013: XIV International Conference on Artificial Intelligence and Law
, pages 92–100. ACM, 2013.
In this paper we investigate how to categorize text excerpts from Italian normative texts. Although text categorization is a problem of broader interest, we single out a specific issue. Namely, we are concerned with categorizing the set of subjects in which Italian Regions are allowed to produce norms: this is the so-called residual legislative power problem. It basically consists in making explicit a set of subjects that was originally defined only in a residual and negative fashion. The categorization of legal text fragments is acknowledged to be a difficult problem, featured by abstract concepts along with a variety of locutions used to denote them, by convoluted sentence structure, and by several other facets. In addition, in the present case subjects are often partially overlapped, and a training set of sufficient size (for the problem under consideration) does not exist: all these aspects make our task challenging. In this setting, classical feature-based approaches provide poor quality results, so we explored algorithms based on compression techniques. We tested three such techniques: we illustrate their main features and report the results of an experimentation where our implementation of such algorithms is compared with the output of standard machine learning algorithms. Far from having found a silver bullet, we show that compression-based techniques provide the best results for the problem at hand, and argue that these approaches can be effectively coupled with more informative and semantically grounded ones.
D. Gianfelice, L. Lesmo, M. Palmirani, D. Perlo, and D. P. Radicioni. Modificatory Provisions Detection: a Hybrid NLP Approach. In B. Verheij, editor, Proceedings of ICAIL 2013: XIV International Conference on Artificial Intelligence and Law
, pages 43–52. ACM, 2013.
In the last few years University of Turin and CIRSFID University of Bologna collaborated to pair NLP techniques and legal knowledge to detect modificatory provisions in normative texts. Annotating these modifications is a relevant and interesting problem, in that modifications affect the whole normative system; and legal language, though more regular than unrestricted language, is sometimes particularly convoluted, and poses specific linguistic issues. This paper focuses on two major aspects. First, we explore a combination between parsing and regular expressions; to the best of our knowledge, such hybrid strategy has never been proposed before to tackle the problem at hand. Secondly, we significantly extend past works coverage (basically focussed on substitution, integration and repeal modifications) in order to account for further twelve modification kinds. For the sake of conciseness, we fully illustrate and discuss only few modification types that are more relevant and interesting: suspension, prorogation of efficacy, postponement of efficacy and exception/derogation. These sorts of modifications appear particularly challenging, in that modifications in these categories make use of similar linguistic speech acts and verbs, and exhibit strong similarities in the linguistic syntactical patterns, to such an extent that to discern them is difficult for the legal expert, too. We describe the implemented system and report about an extensive experimentation on the new modificatory provisions. Results are discussed in order to improve both system's accuracy and annotation practice.
L. Lesmo, A. Mazzei, M. Palmirani, and D. P. Radicioni. TULSI: an NLP System for Extracting Legal Modificatory Provisions. Artificial Intelligence and Law
, 12(4):1–34, 2012.
In this work we present the TULSI system (so named after Turin University Legal Semantic Interpreter), a system to produce automatic annotations of normative documents through the extraction of modificatory provisions. TULSI relies on a deep syntactic analysis and a shallow semantic interpreter that are illustrated in detail. We report the results of an experimental evaluation of the system and discuss them, also suggesting future directions for further improvement.
E. Acotto and D. P. Radicioni. Musical Relevance: a Computational Approach. In Proceedings of the 34th International Conference of the Cognitive Science Society
, pages 1248–1253, Sapporo, Japan, 2012. Cognitive Science Society.
This study is a first attempt at formalizing the concept of Musical Relevance from a cognitive and computational perspective. We elaborate on Sperber and Wilson’s Relevance Theory, and extend it to account for musical cognition, involving both listening and understanding. Our claim is that the application of the concept of Cognitive Relevance to music would permit us to partially explain hearers’ behavior and composers’ choices. A computational model of Musical Relevance could also con- tribute to the formulation of a general computational theory of musical cognition. In turn, formulating an algorithm to compute Musical Relevance can shed light on the computational nature of the broader cognitive principle of relevance. We pro- pose to unify Relevance Theory with the Generative Theory of Tonal Music, in order to compute Musical Relevance. We started implementing a system to test the proposed approach over simple examples and report about the results in a preliminary experimentation.
V. Marcenò, A. Mastropaolo, F. Pallante, and D. P. Radicioni. SENTNET, un sistema per l’analisi delle pronunce della Corte Costituzionale applicato al bilanciamento. Informatica e diritto
, 38(2):187–211, 2012.
L. Robaldo, L. Lesmo, and D. P. Radicioni. Compiling Regular Expressions to Extract Legal Modifications. In B. Schafer, editor, Proceedings of the The 25th International Conference on Legal Knowledge and Information Systems
(Jurix 2012), Amsterdam, Netherlands, 2012. IOS Press.
In this paper we present a prototype for automatically identifying and classifying types of modifications in Italian legal text. The prototype is part of the Eunomos system, a legal knowledge management service that integrates and makes available legislation from various sources, while finding definitions and explanations of legal concepts in a given context. The design of the prototype is grounded on the error analysis of a previous prototype. The latter made use of dependency relations provided by the TUP parser, a multi-purpose parser for Italian. Since those syntactic relations were responsible of the majority of errors, we decided in the present tool to ignore them, and to rewrite an ad-hoc shallow parsing, based on the morphological analysis of the legal text (still provided by the TUP parser). We obtained performances much greater than those of the initial prototype. In particular, the level of precision of the classification in output is now close to 100%.
M. Ceci, L. Lesmo, A. Mazzei, M. Palmirani, and D. P. Radicioni. Semantic Annotation of Legal Texts through a FrameNet-Based Approach. In D. Bourcier, P. Casanovas, U. Pagallo, M. Palmirani, and G. Sartor, editors, AI Approaches to the Complexity of Legal Systems, Revised Selected Papers. LNAI, Vol. 7639, pages 245-255, Berlin, 2012. Springer-Verlag.
In this work we illustrate a novel approach for solving an information extraction problem on legal texts. It is based on Natural Language Processing techniques and on the adoption of a formalization that allows coupling domain knowledge and syntactic information. The proposed approach is applied to extend an existing system to assist human annotators in handling normative modificatory provisions –that are the changes to other normative texts–. Such laws 'versioning' problem is a hard and relevant one. We provide a linguistic and legal analysis of a particular case of modificatory provision (the efficacy suspension), show how such knowledge can be formalized in a linguistic resource such as FrameNet, and used by the semantic interpreter.
L. Lesmo, A. Mazzei, and D. P. Radicioni. Ontology based interlingua translation. In A. F. Gelbukh, editor, Proceedings of the 12th International Conference on Computational Linguistics and Intelligent Text Processing
, CICLing 2011, Tokyo, Japan, volume 6609 of Lecture Notes in Computer Science, pages 1-12. Springer, 2011.
In this paper we describe an interlingua translation system from Italian to Italian Sign Language. The main components of this systems are a broad coverage dependency parser, an ontology based semantic interpreter and a grammar-based generator: we provide the description of the main features of these components.
M. Palmirani, M. Ceci, D. P. Radicioni, and A. Mazzei. FrameNet Model of the Suspension of Norms. In T. van Engers, editor, Proceedings of the 13th International Conference on Artificial Intelligence and Law
, pages 189–193, Pittsburgh, PA, 2011. ACM.
One open problem in the AI & Law community is how to provide computers with a basic understanding of legal concepts, and their relationship with legal texts and with the legal lexicon. We propose to add a layer to connect the linguistic description of the provisions to syntactic patterns using FramNet that can be exploited thought NLP tools. A deep-parsing and shallow- semantics approach has been devised to interpret and retrieve the characterizing components of legal modificatory provisions. In this paper we single out the case of efficacy suspension and show how FrameNet approach can provide profit especially to isolate temporal parameters and their interpretation.
L. Lesmo, A. Mazzei, and D. P. Radicioni. Linguistic descriptions in ontology-based machine translation. In Proceedings of EuroCogSci 2011, the European Cognitive Science Conference
. Sophia, Bulgaria, May 2011.
Lesmo, L., Mazzei, A. and Radicioni, D.P., An Ontology Based Architecture for Translation In Proceedings of the 9th International Conference on Computational Semantics
, Oxford, UK, January 12-14, 2011.
In this paper we present some features of an architecture
for the translation (Italian -- Italian Sign Language) that performs
syntactic analysis, semantic interpretation and generation. Such
architecture relies on an ontology that has been used to encode the
domain of weather forecasts as well as information on language as
part of the world knowledge. We present some general issues of the
ontological semantic interpretation and discuss the analysis of
G. Damele, M. Dogliani, A. Mastropaolo, F. Pallante, and D. P. Radicioni. On Legal Argumentation Techniques: Towards a Systematic Approach. In M. A. Biasiotti and S. Faro, editors, From Information to Knowledge – Online Access to Legal Information: Methodologies, Trends and Perspectives
, Frontiers in Artificial Intelligence and Applications, pages 119–127, Amsterdam, Netherlands, 2011. IOS Press.
In this work we present a project for the investigation of the argumentative techniques adopted in the judgements of the Italian Constitutional Court. We provide a taxonomy of the argumentation techniques, we introduce the representation of the judgements, and outline the system to annotate the judgements with arguments and to query the annotated corpus.
Alice Ruggeri, Cristina Battaglino, Gabriele Tiotto, Carlo Geraci, Daniele P. Radicioni, Alessandro Mazzei, Rossana Damiano, Leonardo Lesmo (2011). Where should I put my hands? Planning hand location in sign languages. In: Proc. of Workshop on Computational Models of Spatial Language Interpretation and Generation (CoSLI-2)
. Boston, July 2011, Tilburg: CEUR, vol. 759 , p. 24-31
D. P. Radicioni and R. Esposito. Advances in Music Information Retrieval
, chapter BREVE: an HMPerceptron-Based Chord Recognition System. Studies in Computational Intelligence, Zbigniew W. Ras and Alicja Wieczorkowska (Editors), Springer, 2010.
Tonal harmony analysis is a sophisticated task. It combines general knowledge with contextual cues, and it is concerned with faceted and evolving objects such as musical language, execution style and taste. We present BREVE, a system for performing a particular kind of harmony analysis, chord recognition: music is encoded as a sequence of sounding events and the system should assign the appropriate chord label to each event. The solution proposed to the problem relies on a conditional model, where domain knowledge is encoded in the form of Boolean features. BREVE exploits the recently proposed algorithm CarpeDiem to obtain significant computational gains in solving the optimization problem underlying the classification process. The implemented system has been validated on a corpus of chorales from J.S. Bach: we report and discuss the learnt weights, point out the committed errors, and elaborate on the correlation between errors and growth in the classification times in places where the music is less clearly asserted.
Ajani, G., Boella, G., Lesmo, L., Martin, M. Mazzei, A., Radicioni, D.P. and Rossi, P., " Multilevel Legal Ontologies" in Semantic Processing of Legal Texts
, Simonetta Montemagni (Editor), Springer, 2010.
Esposito, R. and Radicioni, D. P.,
CarpeDiem: Optimizing the Viterbi Algorithm and Applications to Supervised Sequential Learning
, Journal of Machine Learning Research, 10(Aug):1851-1880, 2009.
The growth of information available to learning systems and the increasing complexity of learning tasks determine the need for devising algorithms that scale well with respect to all learning parameters. In the context of supervised sequential learning, the Viterbi algorithm plays a fundamental role, by allowing the evaluation of the best (most probable) sequence of labels with a time complexity linear in the number of time events, and quadratic in the number of labels.
In this paper we propose CarpeDiem, a novel algorithm allowing the evaluation of the best possible sequence of labels with a sub-quadratic time complexity. We provide theoretical grounding together with solid empirical results supporting two chief facts. CarpeDiem always finds the optimal solution requiring, in most cases, only a small fraction of the time taken by the Viterbi algorithm; meantime, CarpeDiem is never asymptotically worse than the Viterbi algorithm, thus confirming it as a sound replacement.
Lesmo, L., Mazzei, A. and Radicioni, D.P. Legal Modificatory Provisions and Thematic Relations. In Proceedings of ICON-2009: 7th International Conference on Natural Language Processing
, Hyderabad, India, December 2009. Macmillan Publishers.
This paper describes a system for the semantic annotation of legal texts. In particular, it deals with "modificatory provisions", i.e. instructions, present in a legal text, to modify previous laws, by inserting, deleting or replacing parts of the older text. The paper also reports the results on a preliminary study about the use of thematic relations in marking the role of the relevant components of the modificatory provision under analysis.
Mazzei, A., Radicioni, D.P. and R. Brighi. NLP-based Extraction of Modificatory Provisions Semantics. In Proceedings of the International Conference on Artificial Intelligence and Law, ICAIL09
, pages 50-57, Barcelona, Spain, June 2009. ACM.
In this paper we illustrare a research based on NLP techniques aimed at automatically annotate modificatory provisions. We propose an approach which pairs deep syntactic parsing with rule-based shallow semantic analysis relying on a fine-grained taxonomy of modificatory provisions. The implemented system is evaluated on a large dataset hand-crafted by legal experts; the results are discussed and future directions of the research outlined.
Lesmo, L., Mazzei, A., and Radicioni, D.P.. Extracting Semantic Annotations from Legal Texts. In Proceedings of the International Conference on Hypertext
, HT09, 167-172 Turin, Italy, July 2009. ACM.
Esposito, R. and Radicioni, D. P., Empirical Assessment of Two Strategies for Optimizing the Viterbi Algorithm
. In R. Serra and R. Cucchiara (Eds.), AI*IA 2009: 10th Congress of the Italian Association for Artificial Intelligence, 141-150, LNAI 5883, Berlin, 2009. Springer-Verlag.
The Viterbi algorithm is widely used to evaluate sequential classifiers. Unfortunately, depending on the number of labels involved, its time complexity can still be too high for practical purposes. In this paper, we empirically compare two approaches to the optimization of the Viterbi algorithm: Viterbi Beam Search and CarpeDiem. The algorithms are illustrated and tested on datasets representative of a wide range of experimental conditions. Results are reported and the conditions favourable to the characteristics of each approach are discussed.
Lesmo, L., Mazzei, A. and Radicioni, D. P., Semantic Annotation of Legal Modificatory Provisions
. In R. Serra and R. Cucchiara (Eds.), AI*IA 2009: 10th Congress of the Italian Association for Artificial Intelligence, LNAI 5883, Berlin, 2009. Springer-Verlag.
We consider the task of the automatic semantic annotation of the normative modifications enclosed in Italian legal texts. The system is based on a deep syntactic perser coupled with shallow semantic analysis based on frames.
Ajani, G., Boella, G., Lesmo, L., Martin, M. Mazzei, A., Radicioni, D.P. and Rossi, P., Legal Taxonomy Syllabus version 2.0. In 3rd Workshop on Legal Ontologies and Artificial Intelligence Techniques (LOAIT 2009)
. CEUR, Barcelona, Spain, 2009.
Brighi, R., Lesmo, L., Mazzei, A., Palmirani, M. and
Radicioni, D.P., Towards Semantic Interpretation
of Legal Modifications through Deep Syntactic Analysis
E. Francesconi, G. Sartor, D. Tiscornia (Eds.),
Jurix 2008: The 21st Annual Conference, Frontiers in Artificial
Intelligence and Applications, Volume 189, 202-206, 2008, IOS Press.
We are concerned with the automatic semantic interpretation of legal
modificatory provisions. We propose a novel approach which pairs deep
syntactic parsing and a fine-grained taxonomy of legal modifications.
Although still in a developmental stage, the implemented system can be
used to annotate with meta-information modificatory provisions of
Ajani, G., Boella, G., Lesmo, L., Mazzei, A., Radicioni, D.P.
and Rossi, P., Legal Taxonomy Syllabus: Handling Multilevel Legal
, LangTech 2008, Roma, Italy, 2008.
The Legal Taxonomy Syllabus methodology has been used to represent
legal information at different levels such, e.g., European Directives,
and their transpositions into national legislations. In this paper we
point out the main issues of this approach, and extend it to account
for a further level, the Acquis Principles level.
L. Lesmo, A. Mazzei, and Radicioni, D. P., "Estrazione Automatica di Informazioni Relative alle Modificazioni Normative" in I fondamenti cognitivi del diritto
, a cura di M. Palmirani, A. Rotolo, R. Brighi e M. Martoni, volume 1, pp. 65-87. GEDIT Edizioni, Ottobre 2008, Bologna, ISBN 978-88-6027-074-0.
Questa ricerca ha per oggetto l'interpretazione semantica delle
modifiche giuridiche. L'approccio proposto si basa sull'utilizzo
dell'analisi sintattica profonda (deep syntactic parsing) e su una
tassonomia delle modifiche giuridiche. Il sistema implementato è un
prototipo che può essere utilizzato per l'annotazione con
meta-informazione delle modifiche di documenti giuridici nel formato
Ajani, G., Boella, G., Lesmo, L., Mazzei, A., Radicioni, D.P.
and Rossi, P., Multilevel Legal Ontologies
Proceedings of the Workshop Semantic Processing of Legal Texts
held at the Conference on Language Resources and Evaluation (LREC-08),
Marrakech, Morocco, 2008.
The Legal Taxonomy Syllabus methodology has been used to represent
legal information at different levels such, e.g., European Directives,
and their transpositions into national legislations. In this paper we
point out the main issues of this approach, and extend it to account
for a further level, the Acquis Principles level.
Radicioni, D.P. and Lombardo, V.,
A Constraint-Based Approach for Annotating Music Scores with Gestural
, Constraints, 12(4):405-428, 2007.
The physical gestures that operate music instruments are responsible for the qualities of the sound being produced in a performance. Gestural information is thereby crucial for a model of music performance, paired with a model of sound synthesis where this information is applied. The highly constrained nature of performers' gestures makes this task suitable to be modeled via a constraint-based approach, coupled with a strategy aimed at maximizing the gestural comfort of performers. We illustrate the problem representation, the search strategy and a validation of the model against human performance.
Esposito, R., and Radicioni, D. P.,
CarpeDiem: an Algorithm for the Fast Evaluation
of SSL Classifiers
In Z. Ghahramani (Eds.), Proceedings of the 24th Annual
International Conference on Machine Learning (ICML 2007),
257-264, Corvallis, OR, USA, 2007,
In this paper we present a novel algorithm, CarpeDiem. It significantly improves on the time complexity of Viterbi algorithm, preserving the optimality of the result. This fact has consequences on Machine Learning systems that use Viterbi algorithm during learning or classification. We show how the algorithm applies to the Supervised Sequential Learning task and, in particular, to the HMPerceptron algorithm. We illustrate CarpeDiem in full details, and provide experimental results that support the proposed approach.
Radicioni, D. P., and Esposito, R.,
Tonal Harmony Analysis: a Supervised Sequential Learning
. In R. Basili and M.T. Pazienza (Eds.),
AI*IA 2007: Artificial Intelligence and Human-Oriented
Computing, 10th Congress of the Italian Association
for Artificial Intelligence, 638-649, Roma, Italy, 2007, Springer-Verlag.
We have recently presented CarpeDiem, an algorithm that
can be used for speeding up the evaluation of Supervised Sequential
Learning (SSL) classifiers. CarpeDiem provides impressive time
performance gain over the state-of-art Viterbi algorithm when
applied to the tonal harmony analysis task. Along with interesting
computational features, the algorithm reveals some properties that
are of some interest to
Cognitive Science and Computer Music. To explore the question whether
and to what extent the implemented system is suitable
for cognitive modeling,
we first elaborate about its design principles, and then assess the
quality of the analyses produced. A threefold experimentation
reviews the learned weights, the classification errors,
and the search space in
comparison to the actual problem space; data about these points are
reported and discussed.
Esposito, R., and Radicioni, D. P.,
Trip Around the HMPerceptron Algorithm: Empirical Findings and
. In R. Basili and M.T. Pazienza (Eds.),
AI*IA 2007: Artificial Intelligence and Human-Oriented
Computing, 10th Congress of the Italian Association
for Artificial Intelligence, 242-253, Roma, Italy, 2007, Springer-Verlag.
In a recent work we have carried out CarpeDiem, a novel algorithm
for the fast evaluation of Supervised Sequential Learning (SSL)
classifiers. In this paper we point out some interesting
of the learning behavior of the HMPerceptron algorithm that affect
CarpeDiem performances. This observation is the starting point of an
investigation about the internal working of the HMPerceptron,
crucial details of the internal working of the HMPerceptron learning
strategy. The understanding of these details, augment
of the algorithm meanwhile suggesting further enhancements.
Radicioni, D. P. and Botta, M.,
A Methodological Contribution to Music Sequences Analysis
In F. Esposito and Z. W. Ras (Eds.), Foundations
of Intelligent Systems, LNAI 4203, 16th International Symposium on
Methodologies for Intelligent Systems, 409-418, Bari, Italy,
In this paper we present a stepwise method for the analysis of musical
sequences. The starting point is either a MIDI file or the score
of a piece of music. The result is a set of likely themes and motifs.
The method relies on a pitch intervals representation of music and an
event discovery system that extracts significant and repeated
patterns from sequences. We report and discuss the results of a
preliminary experimentation, and outline future enhancements.
Radicioni, D. P. and Esposito, R., A Conditional Model for Tonal Analysis
. In F. Esposito and Z. W. Ras (Eds.), Foundations of Intelligent Systems, LNAI 4203, 16th International Symposium on Methodologies for Intelligent Systems, 652-661, Bari, Italy, 2006, Springer-Verlag.
Tonal harmony analysis is arguably one of the most sophisticated tasks that musicians deal with. It combines general knowledge with contextual cues, being ingrained with both faceted and evolving objects, such as musical language, execution style, or even taste. In the present work we introduce BREVE, a system for tonal analysis. BREVE automatically learns to analyse music using the recently developed framework of conditional models. The system is presented and assessed on a corpus of Western classical pieces from the 18th to the late 19th Centuries repertoire. The results are discussed and interesting issues in modeling this problem are drawn.
Radicioni, D. P. and Esposito, R., Learning Tonal Harmony from Bach Chorales
. In D. Fum, F. del Missier and A. Stocco, (Eds.), Proceedings of the 7th International Conference on Cognitive Modeling, 238-243, Trieste, Italy, 2006.
Tonal harmony analysis is an intriguing cognitive skill, combining general domain knowledge with contextual cues. In this work we cast it to a Supervised Sequential Learning problem (SSL), and introduce a system, showing how such a problem can be solved via the HMPerceptron algorithm. We explain the main concepts used in music analysis, their use within an automatic environment, and provide their cognitive motivation, such that the system is both effective on a computational viewpoint, and justifiable on musical and psychological accounts. Our system's predictions are evaluated on a corpus of 4-parts harmonized chorals by J.S. Bach. We report on the experiment, and discuss our system's results in comparison to literature.
Radicioni, D. P., Computational Modeling of Fingering in Music Performance
, PhD Thesis, Centro di Scienza Cognitiva, Università degli Studi di Torino, Torino, Italy, 2006.
Esposito, R. and Radicioni, D. P., A Fast Alternative to Viterbi Algorithm and its Applications to Supervised Sequential Learning
, Technical Report No. RT 98/06, Università degli Studi di Torino, 2006.
Radicioni, D. P. and Lombardo, V., Computational Modeling of Chord Fingering for String Instruments
, Marr Prize Honorable Mention
,in B. G. Bara, L. Barsalou & M. Bucciarelli (Eds.), Proceedings of the 27th International Conference of the Cognitive Science Society, 1791-1796, Stresa, Italy, 2005, Lawrence Erlbaum Associates.
Fingering is a cognitive process that maps each note on a music score to a fingered position on some instrument. This paper presents a computational model for the fingering process with string instruments, based on a constraint satisfaction approach. The model is implemented in a computer program, which has been tested in an experiment in comparison with three human experts. The results have confirmed the predictions based on a set of constraints that encode the bio-mechanical aspects of the performer's hand in its interaction with the musical instrument.
Radicioni, D. P. and Lombardo, V., Fingering for Music Performance
, Proceedings of the International Computer Music Conference (ICMC2005), 527-530, Barcelona, 2005.
Radicioni, D. P. and Lombardo, V., A CSP Approach for Modeling the Hand Gestures of a Virtual Guitarist
. In S. Bandini and S. Manzoni (Eds.), AI*IA 2005: Advances in Artificial Intelligence, 9th Congress of the Italian Association for Artificial Intelligence, LNAI 3673, 470-473, 2005, Springer-Verlag.
This work presents a model for computing hand gestures of guitarists, within a broader system for the automatic performance of music scores. The fingering model encapsulates the main physical and bio-mechanical constraints that guitarists deal with in their daily practice, and is based on the CSP framework. It is interfaced with a physical model of the classical guitar, which uses the fingering to compute some sound synthesis parameters. We report on a preliminary test, where the fingerings computed by the model are compared with those provided by three human experts.
Radicioni, D. P. and Esposito, R., Sequential Learning for Tonal Analysis
, Technical Report No. RT 92/05, Università degli Studi di Torino, 2005.
Radicioni, D. P., Anselma, L. and Lombardo, V.,
A Segmentation-based Prototype to Compute String Instruments Fingering
. In R. Parncutt, A. Kessler & F. Zimmer (Eds.) Proceedings of the Conference on Interdisciplinary Musicology (CIM04) Graz, Austria, 2004.
Radicioni, D. P., Anselma, L. and Lombardo, V., An
Algorithm to Compute Fingering for String Instruments
. In Atti del
II Convegno AISC, Associazione Italiana di Scienze Cognitive, Ivrea, Italy,
Radicioni, D. P. and Lombardo, V., Computational Modeling of Chord Shapes in Guitar Fingering
, Poster & Demo, 5th International Workshop on Gesture and Sign Language based Human-Computer Interaction, Genova, Italy, 2003.
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