Word order variation and string similarity algorithm to reduce pattern scripting in pattern matching conversational agents

Kaleem, Mohammed; O’Shea, James; Crockett, Keeley

doi:10.1109/ukci.2014.6930180

Cited by 4 publications

(3 citation statements)

References 24 publications

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“…Through empirical evaluation of two versions of the system, the use of an STSM reduced the number of unrecognised human utterances to 5.4% compared to 38% in the pattern scripted version and, hence, the systems incorrect responses were reduced to 3.6% compared to 10.2% in the pattern scripted version [4]. Similar improvements on the benefits of utilising a STSM within DS are also reported in [23]. In this paper, we will replace the traditional semantic similarity measure with a Fuzzy semantic similarity measure to evaluate the effectiveness of a DS through a reduction in the incorrect responses and unrecognised human utterances compared with using an STSM.…”

Section: B) Semantic Similarity Measuresmentioning

confidence: 66%

Interpreting Human Responses in Dialogue Systems using Fuzzy Semantic Similarity Measures

Adel

Crockett

Chandran

et al. 2020

2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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Dialogue systems are automated systems that interact with humans using natural language. Much work has been done on dialogue management and learning using a range of computational intelligence based approaches, however the complexity of human dialogue in different contexts still presents many challenges. The key impact of work presented in this paper is to use fuzzy semantic similarity measures embedded within a dialogue system to allow a machine to semantically comprehend human utterances in a given context and thus communicate more effectively with a human in a specific domain using natural language. To achieve this, perception based words should be understood by a machine in context of the dialogue. In this work, a simple question and answer dialogue system is implemented for a café customer satisfaction feedback survey. Both fuzzy and crisp semantic similarity measures are used within the dialogue engine to assess the accuracy and robustness of rule firing. Results from a 32 participant study, show that the fuzzy measure improves rule matching within the dialogue system by 21.88% compared with the crisp measure known as STASIS, thus providing a more natural and fluid dialogue exchange.

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Section: B) Semantic Similarity Measuresmentioning

confidence: 66%

Interpreting Human Responses in Dialogue Systems using Fuzzy Semantic Similarity Measures

Adel

Crockett

Chandran

et al. 2020

2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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“…Kaleem et al [20] presented a sentence similarity approach formed to mitigate the issue of free word order in the Urdu language. The main objective behind it was to alleviate the complex word order issue that comes with the Urdu language by matching all possible word order variations on a single scripted pattern to reduce the time and effort required to script an Urdu conversational agent.…”

Section: Literature Reviewmentioning

confidence: 99%

Textual Similarity Measurement Approaches: A Survey (1)

Abo-Elghit

Al-Zoghby

Hamza

2020

The Egyptian Journal of Language Engineering

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Survey research is appropriate and necessary to address certain research question types. This paper aims to provide a general overview of the textual similarity in the literature. Measuring textual similarity tends to have an increasingly important turn in related topics like text classification, recovery of specific information from data, clustering, topic retrieval, subject tracking, question answering, essay grading, summarization, and the nowadays trending Conversational Agents (CA), which is a program deals with humans through natural language conversation. Finding the similarity between terms is the essential portion of textual similarity, then used as a major phase for sentence-level, paragraph-level, and script-level similarities. In particular, we concern with textual similarity in Arabic. In the Arabic Language, applying Natural language Processing (NLP) tasks are very challenging indeed as it has many characteristics, which are considered as confrontations. However, many approaches for measuring textual similarity have been presented for Arabic text reviewed and compared in this paper.

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“…The bottom line was that the combined and combined methods resulted in greater results and efficiency. In one of the studies, a hybrid method was used which combines lexical similarity methods to compute the similarity between documents using the user's expressions on the one hand and the text patterns on the other employing the Urdu language [59]. The Levenshtein algorithm was used to measure the similarity between two strings.…”

Section: Hybrid-based Similaritymentioning

confidence: 99%

Arabic Language Documents’ Similarity and its Challenges (A Review)

Saleh¹,

Fadzli²,

El-Ebiary³

2020

IJETT

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Measuring the similarity of documents is of great importance and has influences on many areas and subjects such as Information Retrieval (IR), redrafting discovered, classified documents, and conversational agent programs. Recently, several scientific studies have been interested in implementing the similarity of documents in Arabic language applications. In this study, we review most of the techniques used in measuring the similarity between documents in Arabic; these techniques are classified into three types, mainly lexical, semantic, and finally hybrid. Hence, most of the techniques and methods adopted to measure the similarity between documents were reviewed to determine the most appropriate ones to measure the similarity in Arabic. However, the measurement of the percentage of similarity in documents in the Arabic language represents a major challenge faced by most of the researchers due to the nature of the Arabic language, such as complex morphological processes, ambiguity, and a general lack of resources.

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Word order variation and string similarity algorithm to reduce pattern scripting in pattern matching conversational agents

Cited by 4 publications

References 24 publications

Interpreting Human Responses in Dialogue Systems using Fuzzy Semantic Similarity Measures

Interpreting Human Responses in Dialogue Systems using Fuzzy Semantic Similarity Measures

Textual Similarity Measurement Approaches: A Survey (1)

Arabic Language Documents’ Similarity and its Challenges (A Review)

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