## 2 Research Background
### NLP Applications in Construction  
To avoid unnecessary changes, rework, and potential claims, contractors must thoroughly analyze construction contracts and specifications to ensure that client requirements are identified, managed, and fulfilled [13]. The traditional approach of identifying reporting requirements involves the manual reading, interpretation, and analysis of hundreds of unstructured textual contract pages to differentiate between statements related to requirements and other extraneous text (e.g., instructions and definitions). Techniques capable of accelerating the reporting requirement extraction process, therefore, represent a key prerequisite for the development of an automated time-cost prediction model.  
Natural Language Processing (NLP) is an area of Artificial Intelligence (AI) that focuses on the development of techniques to analyze, process, and extract information from natural human language. Applications include machine translation, speech recognition, and automated content analysis [21]. In construction, a large number of project documents are generated in text format [22]. The use of NLP techniques to organize and improve access to information contained in these types of documents is becoming ever more essential for effective construction management [7], with NLP techniques being increasingly applied in construction research [22; 23; 24]. [PERSON] et al. [7], for instance, employed NLP techniques to automate the classification of construction documents to improve organization of and access to information within interorganizational systems. [PERSON] and [PERSON] [25] also developed an automated classification system of construction documents according to their semantic relationships. [PERSON] and [PERSON] [23] used NLP for the automatic retrieval of similar cases from an electronic case repository of construction accidents.  
Text classification, a subfield of NLP, is an automated process for classifying text into categories [26; 27]. Text classification is divided into rule-based and Machine Learning (ML)-based methods [26]. Rule-based text classification categorizes text using a manually defined pattern to create rules; in contrast, under ML-based text classification, a machine learns how to classify text on its own using data. A variety of text classification models have been developed for the construction domain. For example, [PERSON] and [PERSON] [28] developed a hybrid semantic, multilabel ML-based text classification algorithm for classifying clauses and subclauses of general conditions to support automated compliance checking. [PERSON] et al. [5] proposed a rule-based model to automatically detect risk-related sentences of contracts to support contract risk management for construction contractors. [PERSON] et al. [29] combined NLP and convolutional neural networks to develop a classification model capable of automatically classifying accident narratives to support safety management on site. [PERSON] and [PERSON] [30] proposed an ontology-based, multilabel text classification approach for classifying environmental regulatory clauses to support automated compliance checking in construction, and [PERSON] and [PERSON] [4] proposed a domain-specific classification model to identify client requirements from construction contracts. It is important to note that the implementation of existing text classifiers to different applications remains limited, as text classification models, text features, and performance requirements vary greatly across domains and applications [28]. Designed for a specific domain, the aforementioned text classifiers and are not well-suited for applications that require alternate classification structures.