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As the number, size and complexity of building construction projects increase, code compliance checking becomes more challenging because of the time-consuming, costly, and error-prone nature of a manual checking process. A fully automated code compliance checking would be desirable in facilitating a more efficient, cost effective, and human error-proof code checking. Such automation requires automated information extraction from building designs and building codes, and automated information transformation to a format that allows automated reasoning. Natural Language Processing (NLP) is an important technology to support such automated processing of building codes, because building codes are represented in natural language texts. Part-of-speech (POS) tagging, as an important basis of NLP tasks, must have a high performance to ensure the quality of the automated processing of building codes in such a compliance checking system. However, no systematic testing of existing POS taggers on domain specific building codes data have been performed. To address this gap, the authors analyzed the performance of seven state-of-the-at POS taggers on tagging building codes and compared their results to a manually-labeled gold standard. The authors aim to: (1) find the best performing tagger in terms of accuracy, and (2) identify common sources of errors. In providing the POS tags, the authors used the Penn Treebank tagset, which is a widely used tagset with a proper balance between conciseness and information richness. An average accuracy of 88.80% was found on the testing data. The Standford coreNLP tagger outperformed the other taggers in the experiment. Common sources of errors were identified to be: (1) word ambiguity, (2) rare words, and (3) unique meaning of common English words in the construction context. The found result of machine taggers on building codes calls for performance improvement, such as error-fixing transformational rules and machine taggers that are trained on building codes. 

Despite its importance in central nervous system development, development of the human neural tube (NT) remains poorly understood, given the challenges of studying human embryos, and the developmental divergence between humans and animal models. We report a human NT development model, in which NT-like tissues, neuroepithelial (NE) cysts, are generated in a bioengineered neurogenic environment through self-organization of human pluripotent stem cells (hPSCs). NE cysts correspond to the neural plate in the dorsal ectoderm and have a default dorsal identity. Dorsal-ventral (DV) patterning of NE cysts is achieved using retinoic acid and/or sonic hedgehog and features sequential emergence of the ventral floor plate, P3, and pMN domains in discrete, adjacent regions and a dorsal territory progressively restricted to the opposite dorsal pole. This hPSC-based, DV patterned NE cyst system will be useful for understanding the self-organizing principles that guide NT patterning and for investigations of neural development and neural disease.