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2018, Vernon Press eBooks
Perspectives on the construction of meaning and knowledge: The linguistic, pragmatic, ontological and computational dimension
CHAPTER 15 - FUNCTIONAL LINGUISTIC BASED MOTIVATIONS FOR A CONVERSATIONAL SOFTWARE AGENT2018 •
This chapter discusses a linguistically orientated model of a conversational software agent (CSA) (Panesar 2017) framework sensitive to natural language processing (NLP) concepts and levels of adequacy of a functional linguistic theory (LT). We discuss the relationship between NLP and knowledge representation (KR), and connect this with the goals of a LT (Van Valin and LaPolla 1997), in particular Role and Reference Grammar (RRG) (Van Valin Jr 2005). We debate the advantages of RRG and consider its fitness and computational adequacy. We present a design of a computational model of the linking algorithm that utilises a speech act construction as a grammatical object (Nolan 2014, Nolan 2014) and the sub-model of belief-desire and intentions (BDI) (Rao and Georgeff 1995). This model has been successfully implemented in software, using resource description framework (RDF), and we highlight some implementation issues that arose at the interface between language and knowledge representation (Panesar 2017).
Journal of Computer-Assisted Linguistic Research
An Evaluation Of A Linguistically Motivated Conversational Software Agent Framework2019 •
This paper presents a critical evaluation framework for a linguistically motivated conversational software agent (CSA). The CSA prototype investigates the integration, intersection and interface of the language, knowledge, and speech act constructions (SAC) based on a grammatical object, and the sub-model of belief, desires and intention (BDI) and dialogue management (DM) for natural language processing (NLP). A long-standing issue within NLP CSA systems is refining the accuracy of interpretation to provide realistic dialogue to support human-to-computer communication. This prototype constitutes three phase models: (1) a linguistic model based on a functional linguistic theory – Role and Reference Grammar (RRG), (2) an Agent Cognitive Model with two inner models: (a) a knowledge representation model, (b) a planning model underpinned by BDI concepts, intentionality and rational interaction, and (3) a dialogue model. The evaluation strategy for this Java-based prototype is multi-appro...
Journal of Computer-Assisted Linguistic Research
Conversational artificial intelligence - demystifying statistical vs linguistic NLP solutionsThis paper aims to demystify the hype and attention on chatbots and its association with conversational artificial intelligence. Both are slowly emerging as a real presence in our lives from the impressive technological developments in machine learning, deep learning and natural language understanding solutions. However, what is under the hood, and how far and to what extent can chatbots/conversational artificial intelligence solutions work – is our question. Natural language is the most easily understood knowledge representation for people, but certainly not the best for computers because of its inherent ambiguous, complex and dynamic nature. We will critique the knowledge representation of heavy statistical chatbot solutions against linguistics alternatives. In order to react intelligently to the user, natural language solutions must critically consider other factors such as context, memory, intelligent understanding, previous experience, and personalized knowledge of the user. We...
Journal of Computer-Assisted Linguistic Research
Conversational Artificial Intelligence (AI) -demystifying statistical vs linguistic NLP Solutions2020 •
This paper aims to demystify the hype and attention on Chatbots and its association with conversational artificial intelligence. Both are slowly emerging as a real presence in our lives from the impressive technological developments in machine learning, deep learning and natural language understanding solutions. However, what is under the hood, and how far and to what extent can Chatbots/conversational artificial intelligence solutions work-is our question. Natural language is the most easily understood knowledge representation for people, but certainly not the best for computers because of its inherent ambiguous, complex and dynamic nature. We will critique the knowledge representation of heavy statistical Chatbot solutions against linguistics alternatives. In order to react intelligently to the user, natural language solutions must critically consider other factors such as context, memory, intelligent understanding, previous experience, and personalized knowledge of the user. We will delve into the spectrum of conversational interfaces and focus on a strong artificial intelligence concept. This is explored via a text based conversational software agents with a deep strategic role to hold a conversation and enable the mechanisms need to plan, and to decide what to do next, and manage the dialogue to achieve a goal. To demonstrate this, a deep linguistically aware and knowledge aware text based conversational agent (LING-CSA) presents a proof-of-concept of a non-statistical conversational AI solution (Panesar 2019a, b, 2017).
This paper proposes a linguistically orientated model of a conversational software agent (CSA) (Panesar, 2017) framework sensitive to natural language processing (NLP) concepts and the levels of adequacy of a functional linguistic theory. We discuss the relationship between natural language processing and knowledge representation (KR), and connect this with the goals of a linguistic theory (Van Valin and LaPolla, 1997), in particular Role and Reference Grammar (RRG) (Van Valin Jr, 2005a). We discuss the advantages of RRG and fitness-for-purpose for computational implementation and its level of computational adequacy (Nolan, 2004). We propose a design of a computational model of the linking algorithm that utilises a speech act construction as a grammatical object (Nolan, 2014a, Nolan, 2014b) and the sub-model of belief-desire and intentions (BDI) (Rao and Georgeff, 1995). This model has been successfully implemented in software (Panesar, 2017, Pokahr et al., 2014), using conceptual graphs, and resource description framework (RDF), and we highlight some implementation issues that arose at the interface between language and knowledge representation.
2018 •
7th INTERNATIONAL CONFERENCE ON MEANING AND KNOWLEDGE REPRESENTATION MKR2018 @ ITB Dublin 4, 5 and 6 July, 201
2015 •
Journal of Language and Health
Natural language processing-driven framework for the early detection of language and cognitive decline2023 •
Natural Language Processing (NLP) technology has the potential to provide a non-invasive, cost-effective method using a timely intervention for detecting early-stage language and cognitive decline in individuals concerned about their memory. The proposed pre-screening language and cognition assessment model (PST-LCAM) is based on the functional linguistic model Role and Reference Grammar (RRG) to analyse and represent the structure and meaning of utterances, via a set of language production and cognition parameters. The model is trained on a Dementia TalkBank dataset with markers of cognitive decline aligned to the global deterioration scale (GDS). A hybrid approach of qualitative linguistic analysis and assessment is applied, which includes the mapping of participants´tasks of speech utterances and words to RRG phenomena. It uses a metric-based scoring with resulting quantitative scores and qualitative indicators as pre-screening results. This model is to be deployed in a user-centred conversational assessment platform.
Journal of Computer-Assisted Linguistic Research
Computing the meaning of the assertive speech act by a software agentThis paper examines the nature of the assertive speech act of Irish. We examine the syntactical constructional form of the assertive to identify its constructional signature. We consider the speech act as a construction whose meaning as an utterance depends on the framing situation and context, along with the common ground of the interlocutors. We identify how the assertive speech act is formalised to make it computer tractable for a software agent to compute its meaning, taking into account the contribution of situation, context and a dynamic common ground. Belief, desire and intention play a role in <em>what is meant</em> as against <em>what is said</em>. The nature of knowledge, and how it informs common ground, is explored along with the relationship between knowledge and language. Computing the meaning of a speech act in the situation requires us to consider the level of the interaction of all these dimensions. We argue that the contribution of lexicon a...
Multimedia Tools and Applications
A survey on providing customer and public administration based services using AI: chatbot2022 •
THE AGE OF ARTIFICIAL INTELLIGENCE: An Exploration
A Human Touch in Computer-Generated Literature2020 •
Rejuvenation research
Dialogue Systems and Conversational Agents for Patients with Dementia: the human-robot interaction2018 •
Computer Speech & Language
Some background on dialogue management and conversational speech for dialogue systems2011 •
2020 •
2010 •
Proceedings of the 2010 …
VCA: an experiment with a multiparty virtual chat agent2010 •
2014 •
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2020 •
2017 International Conference on Asian Language Processing (IALP)
Towards improving the performance of chat oriented dialogue systemAAAI Fall Symposium …
CARDIAC: An Intelligent Conversational Assistant for Chronic Heart Failure Patient Heath Monitoring2009 •
Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis
Chatbots for Enterprises: Outlook2009 IEEE International Conference on Bioinformatics and Biomedicine Workshop
Speech recognition in a dialog system for patient health monitoring2009 •
International Journal of Speech Technology
Usefulness, localizability, humanness, and language-benefit: additional evaluation criteria for natural language dialogue systems2016 •