WordNet: a lexical database for English
Communications of the ACM
ELIZA—a computer program for the study of natural language communication between man and machine
Communications of the ACM
Measurement and evaluation of embodied conversational agents
Embodied conversational agents
Artificial Paranoia: A Computer Simulation of Paranoid Processes
Artificial Paranoia: A Computer Simulation of Paranoid Processes
An Approach for Measuring Semantic Similarity between Words Using Multiple Information Sources
IEEE Transactions on Knowledge and Data Engineering
The blind men and the elephant revisited
From brows to trust
Sentence Similarity Based on Semantic Nets and Corpus Statistics
IEEE Transactions on Knowledge and Data Engineering
An approach to conversational agent design using semantic sentence similarity
Applied Intelligence
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This paper presents a novel Semantic-Based Conversational Agent (SCA). Traditional conversational agents (CA) interpret scripts consisting of structural patterns of sentences, which take no consideration of semantic content. The script writer must therefore anticipate the many variations of input the user may respond with during dialogue. This is evidently a high maintenance task. Furthermore, different script writers possess differing levels of skill and as such this can prove to be an exasperating task. The proposed SCA interprets scripts consisting of natural language sentences by means of a semantic sentence similarity measure. User input is measured semantically against the natural language sentences of the current context in order to respond with an appropriate output string. Such scripting is effortless and alleviates the burden of the traditional pattern-scripted languages. Experiments have involved the use of script writers to demonstrate the use of the language. Results have highlighted the potential of the language and shown improvements on traditional pattern-scripted languages.