Part-of-speech tagging of building codes empowered by deep learning and transformational rules

Xue, Xiaorui; Zhang, Jiansong

doi:10.1016/j.aei.2020.101235

Cited by 26 publications

(6 citation statements)

References 34 publications

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“…The techniques used most frequently are word2vec, topic modeling, and BERT and its variations, followed by a number of computationally less intensive techniques, such as POS, text similarity/co-occurrence, and TF-IDF. As can be seen from Table 2, some studies combined multiple techniques to address a specific element [105][106][107][108][109][110][111][112][113], while some less frequently used or known techniques were discovered in building ICT, building load, and renewable energy. *BERT* represents the original BERT and its variations.…”

Section: Applications Of Nlp To Ped Elementsmentioning

confidence: 99%

“…The authors estimate that, with an accuracy achieving 80%, this approach could assist in the automation of tagging sensor data. In another study, 10 BERTbased and 4 bidirectional language models were compared on POS-tagged building code data to automate regulatory information extraction [108]. A precision of over 95% can be found in a bidirectional LSTM architecture and BERT in combination with error-driven transformational rules.…”

Section: Energy Efficiencymentioning

confidence: 99%

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Perspectives of Machine Learning and Natural Language Processing on Characterizing Positive Energy Districts

Han,

Canli,

Shah

et al. 2024

Buildings

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The concept of a Positive Energy District (PED) has become a vital component of the efforts to accelerate the transition to zero carbon emissions and climate-neutral living environments. Research is shifting its focus from energy-efficient single buildings to districts, where the aim is to achieve a positive energy balance across a given time period. Various innovation projects, programs, and activities have produced abundant insights into how to implement and operate PEDs. However, there is still no agreed way of determining what constitutes a PED for the purpose of identifying and evaluating its various elements. This paper thus sets out to create a process for characterizing PEDs. First, nineteen different elements of a PED were identified. Then, two AI techniques, machine learning (ML) and natural language processing (NLP), were introduced and examined to determine their potential for modeling, extracting, and mapping the elements of a PED. Lastly, state-of-the-art research papers were reviewed to identify any contribution they can make to the determination of the effectiveness of the ML and NLP models. The results suggest that both ML and NLP possess significant potential for modeling most of the identified elements in various areas, such as optimization, control, design, and stakeholder mapping. This potential is realized through the utilization of vast amounts of data, enabling these models to generate accurate and useful insights for PED planning and implementation. Several practical strategies have been identified to enhance the characterization of PEDs. These include a clear definition and quantification of the elements, the utilization of urban-scale energy modeling techniques, and the development of user-friendly interfaces capable of presenting model insights in an accessible manner. Thus, developing a holistic approach that integrates existing and novel techniques for PED characterization is essential to achieve sustainable and resilient urban environments.

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Section: Applications Of Nlp To Ped Elementsmentioning

confidence: 99%

Section: Energy Efficiencymentioning

confidence: 99%

Perspectives of Machine Learning and Natural Language Processing on Characterizing Positive Energy Districts

Han,

Canli,

Shah

et al. 2024

Buildings

View full text Add to dashboard Cite

show abstract

“…Markov chain in applying the MEMM method serves to calculate the probability of an observable sequence of events [27]. If the word weight is known, then the MEMM calculation is carried out using (2). The output results obtained in this process are words and word classes by finding the best probability tags using (3) [1]:…”

Section: Implementation Of the Maximum Entropy Markov Model Algorithmmentioning

confidence: 99%

“…This research will discuss the importance of doing part of speech (POS) tagging in Indonesian. POS tagging is the process of automatically giving word-class labels to a word in a sentence so that it can help in arranging sentences according to good sentence patterns [1], [2].…”

Section: Introductionmentioning

confidence: 99%

Indonesian part of speech tagging using maximum entropy markov model on Indonesian manually tagged corpus

Cahyani

Mustikaningtyas

2022

IJ-AI

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This research discusses the development of a part of speech (POS) tagging system to solve the problem of word ambiguity. This paper presents a new method, namely maximum entropy markov model (MEMM) to solve word ambiguity on the Indonesian dataset. A manually labeled “Indonesian manually tagged corpus” was used as data. Furthermore, the corpus is processed using the entropy formula to obtain the weight of the value of the word being searched for, then calculating it into the MEMM Bigram and MEMM Trigram algorithms with the previously obtained rules to determine the part of speech (POS) tag that has the highest probability. The results obtained show POS tagging using the MEMM method has advantages over the methods used previously which used the same data. This paper improves a performance evaluation of research previously. The resulting average accuracy is 83.04% for the MEMM Bigram algorithm and 86.66% for the MEMM Trigram. The MEMM Trigram algorithm is better than the MEMM Bigram algorithm.

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“…Research on the automated extraction of rules from standards has been carried out since the 1960s (Xue and Zhang, 2021). Rules are accepted principles or instructions that state the way things are or should be done, which normally can be classified as quantitative or qualitative according to their quantifiability (Marsal-Llacuna, 2018).…”

Section: Introductionmentioning

confidence: 99%

Automated Qualitative Rule Extraction Based on Bidirectional Long Short- Term Memory Model

Zhu¹,

Li²,

Xiong³

et al. 2022

Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering

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Digital transformation in the construction industry demands smart compliance checking against relevant standards to ensure high-quality project delivery. Due to the diverse characteristics, the qualitative rule extraction for standards remains labour intensive. Therefore, an efficient and automated rule extraction method is pivotal. The artificial neural network has been widely used for textual feature extraction in recent years. In this paper, the authors construct an automated rule extractor based on a bidirectional Long short-term memory (LSTM) neural network model, which can automate the extraction of qualitative rules in textual standards and achieves an accuracy of 96.5% in actual tests. The automated rule extractor can greatly improve the efficiency of converting unstructured textual rules to structured data. This approach can establish the basis for knowledge mining of qualitative standards as well as the development of large-scale compliance checking systems.

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Part-of-speech tagging of building codes empowered by deep learning and transformational rules

Cited by 26 publications

References 34 publications

Perspectives of Machine Learning and Natural Language Processing on Characterizing Positive Energy Districts

Perspectives of Machine Learning and Natural Language Processing on Characterizing Positive Energy Districts

Indonesian part of speech tagging using maximum entropy markov model on Indonesian manually tagged corpus

Automated Qualitative Rule Extraction Based on Bidirectional Long Short- Term Memory Model

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