Search results for " Latent Semantic Analysis"

Image classification based on 2D feature motifs

2013

The classification of raw data often involves the problem of selecting the appropriate set of features to represent the input data. In general, various features can be extracted from the input dataset, but only some of them are actually relevant for the classification process. Since relevant features are often unknown in real-world problems, many candidate features are usually introduced. This degrades both the speed and the predictive accuracy of the classifier due to the presence of redundancy in the candidate feature set. In this paper, we study the capability of a special class of motifs previously introduced in the literature, i.e. 2D irredundant motifs, when they are exploited as feat…

Knowledge Representation in Empathic Robots-Rappresentazione della conoscenza in robot empatici

2011

In questo articolo si illustra l'architettura cognitiva di un robot umanoide basato sul paradigma della Latent Semantic Analysis (LSA). L'approccio LSA consente la creazione e l'utilizzo di un spazio concettuale multi-dimensionale e data driven. Questo paradigma è un passo verso la simulazione di un comportamento emotivo di un robot che interagisce con gli umani. L'architettura è organizzata in tre aree principali: Subconcettuale, emotivo e comportamentale. La prima area elabora i dati percettivi provenienti dai sensori. La seconda area è lo "spazio concettuale di stati emotivi" che costituisce la rappresentazione sub-simbolica di emozioni. L'ultima area attiva un comportamento semantico la…

Geometric Algebra Rotors for Sub-Symbolic Coding of Natural Language Sentences

2007

A sub-symbolic encoding methodology for natural language sentences is presented. The procedure is based on the creation of an LSA-inspired semantic space and associates rotation operators derived from Geometric Algebra to word bigrams of the sentence. The operators are subsequently applied to an orthonormal standard basis of the created semantic space according to the order in which words appear in the sentence. The final rotated basis is then coded as a vector and its orthogonal part constitutes the sub-symbolic coding of the sentence. Preliminary experimental results for a classification task, compared with the traditional LSA methodology, show the effectiveness of the approach.

An architecture with a mobile phone interface for the interaction of a human with a humanoid robot expressing emotions and personality

2011

In this paper is illustrated the cognitive architecture of a humanoid robot based on the proposed paradigm of Latent Semantic Analysis (LSA). This paradigm is a step towards the simulation of an emotional behavior of a robot interacting with humans. The LSA approach allows the creation and the use of a data driven high-dimensional conceptual space. We developed an architecture based on three main areas: Sub-conceptual, Emotional and Behavioral. The first area analyzes perceptual data coming from the sensors. The second area builds the sub-symbolic representation of emotions in a conceptual space of emotional states. The last area triggers a latent semantic behavior which is related to the h…

Automatic Dictionary Creation by Sub-symbolic Encoding of Words

2006

This paper describes a technique for automatic creation of dictionaries using sub-symbolic representation of words in cross-language context. Semantic relationship among words of two languages is extracted from aligned bilingual text corpora. This feature is obtained applying the Latent Semantic Analysis technique to the matrices representing terms co-occurrences in aligned text fragments. The technique allows to find the “best translation” according to a properly defined geometric distance in an automatically created semantic space. Experiments show an interesting correctness of 95% obtained in the best case.

An emotional humanoid partner

2010

In this paper we propose an emotional humanoid robot based on Latent Semantic Analysis, that exhibits an emotional behaviour in the interaction with human. Latent Semantic Analysis (LSA) paradigm is capable to encode the semantics of words using a statistical computation of a large corpus of text. We illustrate how the creation and the use of this emotional conceptual space allows the building of “Latent Semantic Behaviour” because it simulates the emotional associative capabilities of human beings. The presented approach integrates traditional knowledge representation and intuitive capabilities provided by geometric and sub-symbolic information modelling. To validate the effectiveness of t…

AN ARCHITECTURE FOR HUMANOID ROBOT EXPRESSING EMOTIONS AND PERSONALITY

2010

In this paper we illustrate the cognitive architecture of a humanoid robot based on the proposed paradigm of Latent Semantic Analysis (LSA). The LSA approach allows the creation and the use of a data driven high-dimensional conceptual space. This paradigm is a step towards the simulation of an emotional behavior of a robot interacting with humans. The Architecture is organized in three main areas: Sub-conceptual, Emotional and Behavioral. The first area processes perceptual data coming from the sensors. The second area is the “conceptual space of emotional states” which constitutes the sub-symbolic representation of emotions. The last area activates a latent semantic behavior related to the…

Mix and Match Features: Relevance Feedback and Combined Similarity Metrics

2001

An Architecture for Humanoid Robot Expressing Emotions and Personality

2010

In this presentation we illustrate the cognitive architecture of a humanoid robot based on the proposed paradigm of Latent Semantic Behavior (LSB). LSB is based on the Latent Semantic Analysis (LSA) approach that allow the creation and the use of a data driven high-dimensional conceptual space. This paradigm is a step towards the simulation of an emotional behavior of a robot interacting with humans. The Architecture is organized in three main areas: Sub-Conceptual, Emotional and Behavioral. The first area processes perceptual data coming from the sensors. The second area is the "conceptual space of emotional states" which constitutes the sub-symbolic representation of emotions. The last ar…

An Emphatic Humanoid Robot with Emotional Latent Semantic Behavior

2008

In this paper we propose an Entertainment Humanoid Robot model based on Latent Semantic Analysis, that tries to exhibit an emotional behavior in the interaction with human. Latent Semantic Analysis (LSA), based on vector space allows the coding of the words semantics by specific statistical computations applied to a large corpus of text. We illustrate how the creation and the use of this emotional conceptual space can provide a framework upon which to build “Latent Semantic Behavior” because it simulates the emotionalassociative capabilities of human beings. This approach integrates traditional knowledge representation with intuitive capabilities provided by geometric and sub-symbolic infor…