An evaluation of keyword, string similarity and very shallow syntactic matching for a university admissions processing infobot | Zendy

Peter Hancox | Zendy; Nikolaos Polatidis | Zendy

Open Access

An evaluation of keyword, string similarity and very shallow syntactic matching for a university admissions processing infobot

Author(s) -

Peter Hancox,

Nikolaos Polatidis

Publication year - 2013

Publication title -

computer science and information systems

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.244

H-Index - 24

eISSN - 2406-1018

pISSN - 1820-0214

DOI - 10.2298/csis121202065h

Subject(s) - computer science , scalability , similarity (geometry) , natural language processing , string metric , matching (statistics) , string (physics) , string searching algorithm , information retrieval , meaning (existential) , artificial intelligence , semantic similarity , process (computing) , scale (ratio) , feature (linguistics) , pattern matching , linguistics , database , programming language , psychology , statistics , physics , philosophy , mathematics , quantum mechanics , image (mathematics) , psychotherapist

“Infobots” are small-scale natural language question answering systems drawing inspiration from ELIZA-type systems. Their key distinguishing feature is the extraction of meaning from users’ queries without the use of syntactic or semantic representations. Three approaches to identifying the users’ intended meanings were investigated: keyword based systems, Jaro-based string similarity algorithms and matching based on very shallow syntactic analysis. These were measured against a corpus of queries contributed by users of aWWW-hosted infobot for responding to questions about applications to MSc courses. The most effective system was Jaro with stemmed input (78.57%). It also was able to process ungrammatical input and offer scalability.

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