Case Retrieval Reuse Net (CR2N): An Architecture for Reuse of Textual Solutions

Adeyanju, Ibrahim; Wiratunga, Nirmalie; Lothian, Robert; Sripada, Somayajulu; Lamontagne, Luc

doi:10.1007/978-3-642-02998-1_3

Cited by 10 publications

(4 citation statements)

References 8 publications

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“…As for case reuse, Pérez et al proposed a case-based reasoning scheme to extract and reuse design patterns by introducing a genetic algorithm which was used to optimize combinational logic circuits at the gate level [31]. Adeyanju et al developed a Case Retrieval Reuse Net which could generate annotations to identify reusable text content that needed revision [32]. With regard to case revision, Jin et al proposed a new adaptation method called adaptability-based FCA (AFCA) for solution feature values of retrieved cases by using decision tree technique and similarity values which were derived from a multi-algorithm-oriented hybrid SM strategy [33].…”

Section: Literature Reviewmentioning

confidence: 99%

Developing A Case-Based Reasoning Model for Safety Accident Pre-Control and Decision Making in the Construction Industry

Yang

Liu

et al. 2019

IJERPH

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Case-based reasoning (CBR) has been extensively employed in various construction management areas, involving construction cost prediction, duration estimation, risk management, tendering, bidding and procurement. However, there has been a dearth of research integrating CBR with construction safety management for preventing safety accidents. This paper proposes a CBR model which focuses on case retrieval and reuse to provide safety solutions for new problems. It begins with the identification of case problem attribute and solution attribute, the state of hazard is used to describe the problem attribute based on principles of people’s unsafe behavior and objective’s unsafe state. Frame-based knowledge representation method is adopted to establish the case database from dimensions of slot, facet and facet’s value. Besides, cloud graph method is introduced to determine the attribute weight through analyzing the numerical characteristics of expectation value, entropy value and hyper entropy value. Next, thesaurus method is employed to calculate the similarity between cases including word level similarity and sentence level similarity. Principles and procedures have been provided on case revise and case retain. Finally, a real-world case is conducted to illustrate the applicability and effectiveness of the proposed model. Considering the high potential for pre-control and decision-making of construction safety accident, the proposed model is expected to contribute safety managers to take decisions on prevention measures more efficiently.

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Section: Literature Reviewmentioning

confidence: 99%

Developing A Case-Based Reasoning Model for Safety Accident Pre-Control and Decision Making in the Construction Industry

Yang

Liu

et al. 2019

IJERPH

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“…Machine evaluation of song quality is often challenging due to the multi-faceted criteria. Work in NLG (although less complex than song analysis) has successfully adopted overlap metrics such as BLEU and NIST [10,1]. Although these metrics were not originally intended for use in creative NLG, BLEU has been used to rate generated poetry [5] and results suggest a good correlation with human judgment quality, but only for low BLEU scores (between 0.11 and 0.15).…”

Section: Related Workmentioning

confidence: 99%

“…The strong performance of a grammar-based model performed suggested that a metric which considered the quantity and severity of grammatical errors machine-written song lyrics would be effective. In our work we utilise the popular online grammar correction system "Grammarly" 1 to evaluate text and categorise errors into "major grammatical errors" and "minor grammatical errors". Thereafter a metric is used to aggregate these errors to form the quality score.…”

Section: A Grammar Based Quality Metricmentioning

confidence: 99%

GramError: A Quality Metric for Machine Generated Songs

Davies

Wiratunga

Martin

2018

Lecture Notes in Computer Science

Self Cite

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This paper explores whether a simple grammar-based metric can accurately predict human opinion of machine-generated song lyrics quality. The proposed metric considers the percentage of words written in natural English and the number of grammatical errors to rate the quality of machine-generated lyrics. We use a state-of-the-art Recurrent Neural Network (RNN) model and adapt it to lyric generation by retraining on the lyrics of 5,000 songs. For our initial user trial, we use a small sample of songs generated by the RNN to calibrate the metric. Songs selected on the basis of this metric are further evaluated using "Turinglike" tests to establish whether there is a correlation between metric score and human judgment. Our results show that there is strong correlation with human opinion, especially at lower levels of song quality. They also show that 75% of the RNN-generated lyrics passed for human-generated over 30% of the time.

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“…The problem of text reuse in textual case-based reasoning has been addressed in different manners. In [1], textual solutions are reused by identifying small chunks of text to be reused from different solutions and aggregating them, which can be seen as a type of compositional adaptation. An inverse approach is also possible, in which a text is reused in whole but parts that should be modified are identified [14].…”

Section: Principlesmentioning

confidence: 99%

Evaluating a Textual Adaptation System

Dufour-Lussier

Lieber

2015

Case-Based Reasoning Research and Development

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This paper presents a CBR method to retrieve and adapt processes represented as instruction texts, as well as the evaluation methodology that we developed to evaluate it. The evaluation process is userbased, blind and comparative. It is less labour intensive than most existing approaches and is more open to a variety of possible solutions to the same query, among other benefits. It also makes it possible to evaluate separately the textual adaptation process and the underlying formal adaptation process. Craqpot, a CBR system that adapts recipe texts, using a case-based process to extract domain knowledge on the fly, is presented and evaluated. We show that it generates recipes of good quality and texts of acceptable quality.

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Case Retrieval Reuse Net (CR2N): An Architecture for Reuse of Textual Solutions

Cited by 10 publications

References 8 publications

Developing A Case-Based Reasoning Model for Safety Accident Pre-Control and Decision Making in the Construction Industry

Developing A Case-Based Reasoning Model for Safety Accident Pre-Control and Decision Making in the Construction Industry

GramError: A Quality Metric for Machine Generated Songs

Evaluating a Textual Adaptation System

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