Abstract
We describe a chatbot performing advertising and social promotion (CASP) to assist in automation of managing friends and other social network contacts. This agent employs a domain-independent natural language relevance technique that filters web mining results to support a conversation with friends and other network members. This technique relies on learning parse trees and parse thickets (sets of parse trees) of paragraphs of text such as Facebook postings. To yield a web mining query from a sequence of previous postings by human agents discussing a topic, we develop a Lattice Querying algorithm which automatically adjusts the optimal level of query generality. We also propose an algorithm for CASP to make a translation into multiple languages plausible as well as a method to merge web mined textual chunks. We evaluate the relevance features, overall robustness and trust of CASP in a number of domains, acting on behalf of the author of this Chapter in his Facebook account in 2014–2016. Although some Facebook friends did not like CASP postings and even unfriended the host, overall social promotion results are positive as long as relevance, style and rhetorical appropriateness is properly maintained.
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References
BBC Inside Science (2014) Automatic Facebook. http://www.bbc.co.uk/programmes/b040lnlf
Bhasker B, Srikumar K (2010) Recommender systems in e-commerce. CUP. ISBN 978-0-07-068067-8
Borgida ER, McGuinness DL (1996) Asking queries about frames. In: Proceedings of the 5th international conference on the principles of knowledge representation and reasoning, pp 340–349
Buchegger S, Datta A (2009) A case for P2P infrastructure for social networks – opportunities & challenges. In: Proceedings of 6th international conference on wireless on-demand network systems and services, Utah, pp 161–168
Buzmakov A (2015) Formal concept analysis and pattern structures for mining structured data. Inria Publication. https://hal.inria.fr/tel-01229062/
Cassell J, Bickmore T, Campbell L, Vilhjálmsson H, Yan H (2000) Human conversation as a system framework: designing embodied conversational agents. In: Cassell J et al (eds) Embodied conversational agents. MIT Press, Cambridge, MA, pp 29–63
Chambers N, Cer D, Grenager T, Hall D, Kiddon C, MacCartney M, de Marneffe C, Ramage D, Yeh E, Manning CD (2007) Learning alignments and leveraging natural logic. In: Proceedings of the ACL-07 workshop on textual entailment and paraphrasing
De Rosis F, Pelachaud C, Poggi I, Carofiglio V, de Carolis B (2003) From Greta’s mind to her face: modeling the dynamics of affective states in a conversational embodied agent. Int J Hum Comput Stud 59:81–118
Dias J, Paiva A (2005) Feeling and reasoning: a computational model for emotional characters. In: EPIA affective computing workshop, Springer
Galitsky B (1998) Scenario synthesizer for the internet advertisement. Proc J Conf Infol Sci Duke Univ 3:197–200
Galitsky B (2003) Natural language question answering system: technique of semantic headers. Advanced Knowledge International, Adelaide
Galitsky B (2013) Transfer learning of syntactic structures for building taxonomies for search engines. Eng Appl Artif Intell 26(10):2504–2515
Galitsky B (2014) Learning parse structure of paragraphs and its applications in search. Eng Appl Artif Intell 32:160–184
Galitsky B (2016) Theory of mind engine. In: Computational autism. Springer, Cham
Galitsky B (2017) Content inversion for user searches and product recommendation systems and methods. US Patent 15150292
Galitsky B, Ilvovsky D (2016) Discovering disinformation: discourse-level approach. Fifteenth Russian national AI conference, Smolenks, Russia, pp 23–33
Galitsky B, Kovalerchuk B (2006) Mining the blogosphere for contributor’s sentiments. AAAI Spring symposium on analyzing weblogs. Stanford, CA
Galitsky B, Kuznetsov SO (2008) Learning communicative actions of conflicting human agents. J Exp Theor Artif Intell 20(4):277–317
Galitsky B, Kuznetsov SO (2013) A web mining tool for assistance with creative writing. ECIR, European conference on information retrieval, pp 828–831
Galitsky B, Levene M (2007) Providing rating services and subscriptions with web portal infrastructures. Encyclopedia of portal technologies and applications, pp 855–862
Galitsky B, McKenna EW (2017) Sentiment extraction from consumer reviews for providing product recommendations. US Patent 9,646,078
Galitsky B, Parnis A (2017) How children with autism and machines learn to interact. In: Autonomy and artificial intelligence: a threat or savior? Springer, Cham, pp 195–226
Galitsky B, Shpitsberg I (2016) Autistic learning and cognition. In: Computational autism. Springer, Cham, pp 245–293
Galitsky B, Tumarkina I (2004) Justification of customer complaints using emotional states and mental actions. FLAIRS conference, Miami, Florida
Galitsky B, Usikov D (2008) Programming spatial algorithms in natural language. AAAI workshop technical report WS-08-11, Palo Alto, pp 16–24
Galitsky B, Kuznetsov SO, Samokhin MV (2005) Analyzing conflicts with concept-based learning. Int Conf Concept Struct 3596:307–322
Galitsky B, Dobrocsi G, de la Rosa JL, Kuznetsov SO (2010) From generalization of syntactic parse trees to conceptual graphs. In: Croitoru M, Ferré S, Lukose D (eds) Conceptual structures: from information to intelligence, 18th international conference on conceptual structures, ICCS 2010, Lecture notes in artificial intelligence, vol 6208, pp 185–190
Galitsky B, Dobrocsi G, de la Rosa JL, Kuznetsov SO (2011) Using generalization of syntactic parse trees for taxonomy capture on the web. 19th international conference on conceptual structures, pp 104–117
Galitsky B, de la Rosa JL, Dobrocsi G (2012) Inferring the semantic properties of sentences by mining syntactic parse trees. Data Knowl Eng 81–82(Nov):21–45
Galitsky B, Ilvovsky D, Kuznetsov SO, Strok F (2013) Finding maximal common sub-parse thickets for multi-sentence search. IJCAI workshop on graphs and knowledge representation, IJCAI 2013
Galitsky B, Ilvovsky D, Lebedeva N, Usikov D (2014) Improving trust in automation of social promotion . AAAI Spring symposium on the intersection of robust intelligence and trust in autonomous systems, Stanford, CA, 2014
Jiang JJ, Conrath DW (1997) Semantic similarity based on corpus statistics and lexical taxonomy. In: Proceedings of the international conference on research in computational linguistics, Taiwan
Kushman N, Artzi Y, Zettlemoyer L, Barzilay R (2014) Learning to automatically solve algebra word problems. ACL 2014
Lawless WF, Llinas J, Mittu R, Sofge DA, Sibley C, Coyne J, Russell S (2013) Robust intelligence (RI) under uncertainty: mathematical and conceptual foundations of autonomous hybrid (human-machine-robot) teams, organizations and systems. Struct Dyn 6(2):1–35
Lisetti CL (2008). Embodied conversational agents for psychotherapy. CHI 2008 workshop on technology in mental health, New York
MacCartney B, Galley M, Manning CD (2008) A phrase-based alignment model for natural language inference. The conference on empirical methods in natural language processing (EMNLP-08), Honolulu, HI, October 2008
Makhalova T, Ilvovsky DI, Galitsky B (2015) Pattern structures for news clustering. FCA4AI@ IJCAI, pp 35–42
Montaner M, Lopez B, de la Rosa JL (2003) A taxonomy of recommender agents on the internet. Artif Intell Rev 19(4):285–330
New Scientist (2014) http://www.newscientist.com/article/mg22229634.400-one-per-cent.html
Ourioupina O, Galitsky B (2001) Application of default reasoning to semantic processing under question-answering. DIMACS Tech Report 16
Reeves B, Nass C (1996) The media equation: how people treat computers, television, and new media like real people and places. Cambridge University Press, UK
Sidorov G, Velasquez F, Stamatatos E, Gelbukh A, Chanona-Hernández L (2012) Syntactic N-grams as machine learning features for natural language processing. Expert Syst Appl 41(3):853C860
Strok F, Galitsky B, Ilvovsky D, Kuznetsov SO (2014) Pattern structure projections for learning discourse structures. AIMSA 2014: artificial intelligence: methodology, systems, and applications, pp 254–260
Trias i Mansilla A, de la Rosa i Esteva JL (2011) Asknext: an agent protocol for social search. Inf Sci 2011:186–197
Trias AJL, de la Rosa B, Galitsky G (2010) Drobocsi, automation of social networks with QA agents (extended abstract). In: van der Hoek W, Kaminka GA, Lespérance Y, Luck M, Sen S (eds) Proceedings of 9th international conference on autonomous agents and multi-agent systems, AAMAS ‘10, Toronto, pp 1437–1438
Wu LS, Akavipat R, Maguitman A, Menczer F (2008) Adaptive peer to peer social networks for distributed content based web search. In: Social information retrieval systems: emergent technologies and applications for searching the web effectively. IGI Global, Hershey, pp 155–178
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Galitsky, B. (2019). A Social Promotion Chatbot. In: Developing Enterprise Chatbots. Springer, Cham. https://doi.org/10.1007/978-3-030-04299-8_12
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DOI: https://doi.org/10.1007/978-3-030-04299-8_12
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