Search results for "semantic spaces"
showing 6 items of 6 documents
Effectiveness of Data-Driven Induction of Semantic Spaces and Traditional Classifiers for Sarcasm Detection
2019
Irony and sarcasm are two complex linguistic phenomena that are widely used in everyday language and especially over the social media, but they represent two serious issues for automated text understanding. Many labeled corpora have been extracted from several sources to accomplish this task, and it seems that sarcasm is conveyed in different ways for different domains. Nonetheless, very little work has been done for comparing different methods among the available corpora. Furthermore, usually, each author collects and uses their own datasets to evaluate his own method. In this paper, we show that sarcasm detection can be tackled by applying classical machine learning algorithms to input te…
Una Comunità di Chatbot per i Beni Culturali
2005
L'intuizione occupa un ruolo fondamentale nella capacità di ragionamento degli esseri umani. L'obiettivo del lavoro è la progettazione di un'infrastruttura in grado di fornire informazioni che oltre ad appoggiarsi ad un insieme di conoscenze basato su regole sia dotata anche di capacità intuitive. A tal fine è presentato un sistema preliminare che rappresenta il primo passo verso la realizzazione di tale infrastruttura. Il sistema permette ad un utente di interagire con una comunità di chat-bot aventi competenze specifiche in modo da navigare in uno spazio concettuale generato automaticamente con il paradigma di analisi della semantica latente (LSA). La base di conoscenza di ciascun chat-bo…
A Semantic Layer on Semi-structured Data Sources for Intuitive Chatbots
2009
The main limits of chatbot technology are related to the building of their knowledge representation and to their rigid information retrieval and dialogue capabilities, usually based on simple "pattern matching rules". The analysis of distributional properties of words in a texts corpus allows the creation of semantic spaces where represent and compare natural language elements. This space can be interpreted as a "conceptual" space where the axes represent the latent primitive concepts of the analyzed corpus. The presented work aims at exploiting the properties of a data-driven semantic/conceptual space built using semi-structured data sources freely available on the web, like Wikipedia. Thi…
A Geometric Algebra Based Distributional Model to Encode Sentences Semantics
2013
Word space models are used to encode the semantics of natural language elements by means of high dimensional vectors [23]. Latent Semantic Analysis (LSA) methodology [15] is well known and widely used for its generalization properties. Despite of its good performance in several applications, the model induced by LSA ignores dynamic changes in sentences meaning that depend on the order of the words, because it is based on a bag of words analysis. In this chapter we present a technique that exploits LSA-based semantic spaces and geometric algebra in order to obtain a sub-symbolic encoding of sentences taking into account the words sequence in the sentence. © 2014 Springer-Verlag Berlin Heidel…
A Modular Framework for Versatile Conversational Agent Building
2011
This paper illustrates a web-based infrastructure of an architecture for conversational agents equipped with a modular knowledge base. This solution has the advantage to allow the building of specific modules that deal with particular features of a conversation (ranging from its topic to the manner of reasoning of the chatbot). This enhances the agent interaction capabilities. The approach simplifies the chatbot knowledge base design process: extending, generalizing or even restricting the chatbot knowledge base in order to suit it to manage specific dialoguing tasks as much as possible.
Graph-based exploration and clustering analysis of semantic spaces
2019
Abstract The goal of this study is to demonstrate how network science and graph theory tools and concepts can be effectively used for exploring and comparing semantic spaces of word embeddings and lexical databases. Specifically, we construct semantic networks based on word2vec representation of words, which is “learnt” from large text corpora (Google news, Amazon reviews), and “human built” word networks derived from the well-known lexical databases: WordNet and Moby Thesaurus. We compare “global” (e.g., degrees, distances, clustering coefficients) and “local” (e.g., most central nodes and community-type dense clusters) characteristics of considered networks. Our observations suggest that …