I-ChatbIT: an Intelligent Chatbot for the Italian Language
Authors: Arianna Pipitone, Vincenzo Cannella, Roberto Pirrone (Università di Palermo, 2014)
Topic: Chatbots and Dialogue Systems

Abstract
A novel chatbot architecture for the Italian language is presented that is aimed at implementing cognitive under-standing of the query by locating its cor-respondent subgraph in the agent’s KB by means of a graph matching strategy pur-posely devised. The FCG engine is used for producing replies starting from the se-mantic poles extracted from the candidate answers’ subgraphs. The system imple-ments a suitable disambiguation strategy for selecting the correct answer by analyz-ing the commonsense knowledge related to the adverbs in the query that is embed-ded in the lexical constructions of the ad-verbs themselves as a proper set of fea-tures. The whole system is presented, and a complete example is reported throughout the paper.

Introduction
In recent years the Question-Answering systems (QAs) have been improved by the integration with Natural Language Processing (NLP) techniques, which make them able to interact with humans in a dynamic way: the production of answers is more sophisticated than the classical chatterbots, where some sentence templates are pre-loaded and linked to the specific questions. In this paper we propose a new methodology that integrates the chatterbot technology with the Cog-nitive Linguistics (CL) (Langacker, 1987) princi-ples, with the aim of developing a QA system that is able to harvest a linguistic knowledge from its inner KB, and use it for composing answers dy-namically. Grammatical templates and structures tailored to the Italian language that are construc-tions of the Construction Grammar (CxG) (Gold-berg, 1995) and a linguistic Italian source of verbs have been developed purposely, and used for the NL production. The result of the methodology im-plementation is I-ChatbIT, an Italian chatbot that is intelligent not only for the dynamic nature of the answers, but in the sense of cognitive under-standing and production of NL sentences. Cog-nitive understanding of the NL query is achieved by placing it in the system’s KB, which represents the conceptualization of the world as it has been perceived by the agent. The outcome of such a process is the generation of what we call the mean-ing activation subgraph in the KB. Browsing this subgraph, the system elaborates and detects the content of the answer, that is next grammatically composed through the linguistic base. The FCG engine is then used as the key component for pro-ducing the answer. Summarily, the work reports the modeling of the two tasks outlined above. The paper is arranged as follow: in the next section the most popular chatbots are shown, devoting par-ticular attention to the Italian ones. Section 3 de-scribes the implemented methodology explaining in detail a practical example. Finally, conclusions and future works are discussed in section 4.

Conclusion
A novel chatbot architecture for the Italian language has been presented that is aimed at implementing cognitive understanding of the query by locating its correspondent subgraph in the agent’s KB by means of a GED strategy based on the k- AT algorithm, and the Jaro–Winkler distance. The FCG engine is used for producing replies starting from the semantic poles extracted from the candidate answers’ subgraphs. The system implements a suitable disambiguation strategy for selecting the correct answer by analyzing the commonsense knowledge related to the adverbs in the query that is embedded in the lexical constructions of the adverbs themselves as a proper set of features. Future works are aimed at completing the IVS, and using explicit commonsense knowledge inside the KB for fine disambiguation. Finally, the graph matching strategy will be further tuned.