Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Introduction Read chapter Read first chapter

2019 | OriginalPaper | Chapter

5. Assuring Chatbot Relevance at Syntactic Level

Author : Boris Galitsky

Published in: Developing Enterprise Chatbots

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

In this chapter we implement relevance mechanism based on similarity of parse trees for a number of chatbot components including search. We extend the mechanism of logical generalization towards syntactic parse trees and attempt to detect weak semantic signals from them. Generalization of syntactic parse tree as a syntactic similarity measure is defined as the set of maximum common sub-trees and performed at a level of paragraphs, sentences, phrases and individual words. We analyze semantic features of such similarity measure and compare it with semantics of traditional anti-unification of terms. Nearest neighbor machine learning is then applied to relate a sentence to a semantic class.
Using syntactic parse tree-based similarity measure instead of bag-of-words and keyword frequency approaches, we expect to detect a weak semantic signal otherwise unobservable. The proposed approach is evaluated in four distinct domains where a lack of semantic information makes classification of sentences rather difficult. We describe a toolkit which is a part of Apache Software Foundation project OpenNLP.chatbot, designed to aid search engineers and chatbot designers in tasks requiring text relevance assessment.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Developing Conversational Natural Language Interface to a Database
next chapter Semantic Skeleton Thesauri for Question Answering Bots
Literature
Allen JF (1987) Natural language understanding. Benjamin Cummings, Menlo ParkMATH
Abney S (1991) Parsing by chunks. In: Principle-based parsing. Kluwer Academic Publishers, pp 257–278
Aleman-Meza B. Halaschek C, Arpinar I Sheth A (2003) A Context-Aware Semantic Association Ranking. In: Proceedings of first int’l workshop Semantic Web and Databases (SWDB ‘03), pp. 33-50.
Amiridze N, Kutsia T (2018) Anti-unification and natural language processing fifth workshop on natural language and computer science, NLCS’18, EasyChair Preprint no. 203
Banko M, Cafarella J, Soderland S, Broadhead M, Etzioni O (2007) Open information extraction from the web. In: Proceedings of the twentieth international joint conference on artificial intelligence. AAAI Press, Hyderabad, pp 2670–2676
Bar-Haim R, Dagan I, Greental I, Shnarch E (2005) Semantic inference at the lexical-syntactic level AAAI-05.
Bunke H (2003) Graph-based tools for data mining and machine learning. Lect Notes Comput Sci 2734/2003:7–19CrossRef
Cardie C, Mooney RJ (1999) Machine learning and natural language, Mach Learn 1(5)
Carreras X, Marquez L (2004) Introduction to the CoNLL-2004 shared task: semantic role labeling. In: Proceedings of the eighth conference on computational natural language learning. ACL, Boston, pp 89–97
Chakrabarti D, Faloutsos C (2006) Graph mining: laws, generators, and algorithms. ACM Comput Surv 38(1)
Collins M, Duffy N (2002) New ranking algorithms for parsing and tagging: kernels over discrete structures, and the voted perceptron. In: ACL02
Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297MATH
Pighin D, Moschitti A (2009) Reverse engineering of tree kernel feature spaces. In: Proceedings of the 2009 conference on empirical methods in natural language processing. Association for Computational Linguistics, Singapore, pp 111–120
de Salvo Braz R, Girju R, Punyakanok V, Roth D, Sammons M (2005) An inference model for semantic entailment in natural language, Proc AAAI-05
Ding L, Finin T, Joshi A, Pan R, Cost RS, Peng Y, Reddivari P, Doshi V, Sachs J (2004) Swoogle: a search and metadata engine for the semantic web. In: Proceeding of the 13th ACM International Conference on Information and Knowledge Management (CIKM’04), pp 652–659
Ducheyne S (2008) J.S. Mill’s canons of induction: from true causes to provisional ones. History and Philosophy of Logic 29(4):361–376CrossRef
Durme BV, Huang Y, Kupsc A, Nyberg E (2003) Towards light semantic processing for question answering. HLT Workshop on Text Meaning
Dzikovska M., Swift M, Allen J, William de Beaumont W (2005) Generic parsing for multi-domain semantic interpretation. International Workshop on Parsing Technologies (Iwpt05), Vancouver BC.
Fukunaga K (1990) Introduction to statistical pattern recognition, 2nd edn. Academic Press Professional, Inc., San DiegoMATH
Galitsky B, Josep Lluis de la Rosa, Gabor Dobrocsi (2011a) Building integrated opinion delivery environment. FLAIRS-24, West Palm Beach FL May 2011
Galitsky B, Dobrocsi G, de la Rosa JL, Kuznetsov SO (2011b) Using generalization of syntactic parse trees for taxonomy capture on the web. ICCS:104–117
Galitsky BA, G Dobrocsi, JL De La Rosa, SO Kuznetsov (2010) From generalization of syntactic parse trees to conceptual graphs. International Conference on Conceptual Structures, 185-190.
Galitsky B (2003) Natural language question answering system: technique of semantic headers. Advanced Knowledge International, Australia
Galitsky B, Kuznetsov SO (2008) Learning communicative actions of conflicting human agents. J Exp Theor Artif Intell 20(4):277–317CrossRef
Galitsky B, D Usikov (2008) Programming spatial algorithms in natural language. AAAI Workshop Technical Report WS-08-11.–Palo Alto, pp 16–24
Galitsky B, González MP, Chesñevar CI (2009) A novel approach for classifying customer complaints through graphs similarities in argumentative dialogue. Decision Support Systems 46(3):717–729CrossRef
Galitsky B, De La Rosa JL, Dobrocsi G (2012) Inferring the semantic properties of sentences by mining syntactic parse trees. Data Knowl Eng 81:21–45CrossRef
Galitsky B, Kuznetsov SO, Usikov D (2013) Parse thicket representation for multi-sentence search. In: International conference on conceptual structures, pp 153–172
Galitsky B, Ilvovsky DI, Kuznetsov SO (2014) Extending tree kernels towards paragraphs. Int J Comput Linguist Appl 5(1):105–116
Galitsky B, Botros S (2015) Searching for associated events in log data. US Patent 9,171,037
Galitsky B (2017a) Improving relevance in a content pipeline via syntactic generalization. Eng Appl Artif Intell 58:1–26CrossRef
Galitsky B (2017b) Matching parse thickets for open domain question answering. Data Knowl Eng 107:24–50CrossRef
Ganter B, Kuznetsov S (2001) Pattern Structures and Their Projections, Proceedings of the 9th International Conference on Conceptual Structures, ICCS’01, ed. G. Stumme and H. Delugach, Lecture Notes in Artificial Intelligence, 2120, 129–142.
Garey MR, Johnson DS (1979) Computers and intractability: a guide to the theory of NP-completeness. Freeman, San FranciscoMATH
Gildea D (2003) Loosely tree-based alignment for machine translation. In: Proceedings of the 41th annual conference of the Association for Computational Linguistics (ACL-03), Sapporo, pp 80–87
Iosif E, Potamianos A (2009) Unsupervised semantic similarity computation between terms using web documents. IEEE Trans Knowl Data Eng 13
Kapoor S, Ramesh H (1995) Algorithms for Enumerating All Spanning Trees of Undirected and Weighted Graphs. SIAM J Comput 24:247–265MathSciNetCrossRef
Kok S, Domingos P (2008) Extracting semantic networks from text via relational clustering. In: Proceedings of the nineteenth European conference on machine learning. Springer, Antwerp, Belgium, pp 624–639
Kuznetsov SO, Samokhin, MV (2005) Learning closed sets of labeled graphs for chemical applications. In: Inductive Logic Programming pp 190–208
Lamberti F, Sanna A, Demartini C (2009) A Relation-Based Page Rank Algorithm for Semantic Web Search Engines. IEEE Trans Knowl Data Eng 21(1):123–136CrossRef
Lin D, Pantel P (2001) DIRT: discovery of inference rules from text. In: Proceedings of ACM SIGKDD conference on knowledge discovery and data mining 2001, 323–328
Makhalova T, Ilvovsky DI, Galitsky BA (2015) Pattern Structures for News Clustering. FCA4AI@ IJCAI, 35-42
Mill JS (1843) A system of logic, racionative and inductive, London
Moldovan D, Clark C, Harabagiu S, Maiorano S (2003) Cogex: a logic prover for question answering. In: Proceedings of HLTNAACL 2003
Moreda P, Navarro B, Palomar M (2007) Corpus-based semantic role approach in information retrieval. Data Knowl Eng 61:467–483CrossRef
Moschitti A (2008) Kernel Methods, Syntax and Semantics for Relational Text Categorization. In: Proceeding of ACM 17th Conference on Information and Knowledge Management (CIKM). Napa Valley, California.
Moschitti A, Pighin D, Basili R (2006). Semantic role labeling via tree kernel joint inference. In Proceedings of the 10th conference on computational natural language learning, New York, USA
Plotkin GD (1970) A note on inductive generalization. In: Meltzer B, Michie D (eds) Machine Intelligence, vol 5. Elsevier North-Holland, New York, pp 153–163
Poon H, Domingos P (2008) Joint unsupervised coreference resolution with Markov logic. In: Proceedings of the conference on empirical methods in natural language processing (EMNLP’08). Association for Computational Linguistics, Stroudsburg, pp 650–659CrossRef
Ravichandran D, Hovy E (2002) Learning surface text patterns for a Question Answering system. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL 2002), Philadelphia, PA
Romano L, Kouylekov M, Szpektor I, Dagan I, Lavelli A (2006) Investigating a generic paraphrase-based approach for relation extraction. In: Proceedings of EACL, 409–416
Stevenson M, Greenwood MA (2005) A semantic approach to IE pattern induction. In: Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL 2005), Ann Arbor
Strok F, Galitsky B, Ilvovsky D, Kuznetsov S (2014) Pattern structure projections for learning discourse structures. International Conference on Artificial Intelligence: methodology, Systems, and Applications. Springer, Cham, pp 254–260
Strzalkowski T, Carballo JP, Karlgren J, Tapanainen AHP, Jarvinen T (1999) Natural language information retrieval: TREC-8 report. In: Text Retrieval conference
Suykens JAK, Horvath G, Basu S, Micchelli C, Vandewalle J (Eds.) (2003) Advances in learning theory: methods, models and applications, vol. 190 NATO-ASI series III: computer and systems sciences, IOS Press
Thompson C, Mooney R, Tang L (1997) Learning to parse NL database queries into logical form. In: Workshop on automata induction, grammatical inference and language acquisition
Voorhees EM (2004) Overview of the TREC 2001 Question Answering track. In: TREC
Zanzotto FM, Moschitti A (2006) Automatic learning of textual entailments with cross-pair similarities. In: Proceedings of the Joint 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics (COLING-ACL), Sydney, Australia.
Zhang M, Zhou GD, Aw A (2008) Exploring syntactic structured features over parse trees for relation extraction using kernel methods. Inf Process Manage Int J 44(2):687–701CrossRef
Zhao Y, Shen X, Senuma H, Aizawa A (2018) A comprehensive study: sentence compression with linguistic knowledge-enhanced gated neural network. Data Knowl Eng V117:307–318CrossRef
Zettlemoyer LS, Collins M (2005) Learning to map sentences to logical form: structured classification with probabilistic categorial grammars. In: Bacchus F, Jaakkola T (eds) Proceedings of the twenty-first conference on uncertainty in artificial intelligence (UAI’05). AUAI Press, Arlington, pp 658–666
Metadata
Title
Assuring Chatbot Relevance at Syntactic Level
Author
Boris Galitsky
Copyright Year
2019
Book
Developing Enterprise Chatbots

Print ISBN: 978-3-030-04298-1

Electronic ISBN: 978-3-030-04299-8

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-04299-8_5

Premium Partner

    Image Credits