6533b834fe1ef96bd129d6fc

RESEARCH PRODUCT

Clifford Rotors for Conceptual Representation in Chatbots

Agnese AugelloGiovanni PilatoSalvatore GaglioGiorgio Vassallo

subject

Settore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniDiscrete mathematicsComputer sciencebusiness.industryLatent semantic analysisInformationSystems_INFORMATIONSTORAGEANDRETRIEVALRepresentation (systemics)computer.software_genreChatbotGeometric algebraKnowledge baseArtificial IntelligenceEncoding (semiotics)chatbot clifford algebraArtificial intelligenceDialog systembusinesscomputerNatural language processingNatural language

description

In this abstract we introduce an unsupervised sub-symbolic natural language sentences encoding procedure aimed at catching and representing into a Chatbot Knowledge Base (KB) the concepts expressed by an user interacting with a robot. The chatbot KB is coded in a conceptual space induced from the application of the Latent Semantic Analysis (LSA) paradigm on a corpus of documents. LSA has the effect of decomposing the original relationships between elements into linearly-independent vectors. Each basis vector can be considered therefore as a "conceptual coordinate", which can be tagged by the words which better characterize it. This tagging is obtained by performing a (TF-IDF)-like weighting schema [3], that we call TW-ICW (term weight-inverse conceptual coordinate weight), to weigh the relevance of each term on each conceptual coordinate.

yearjournalcountryeditionlanguage
2013-01-01
https://doi.org/10.1007/978-3-642-34274-5_64