# Development of a Framework to Understand Tables in Engineering Specification Documents
## 2 Previous Works  
Previous works developed different approaches for table functional analysis, table structural analysis, and table interpretation [11]. Each approach was depending on its context and the source of tables. Table 1 shows approaches of previous works and their application context. [PERSON] et al. [9] suggested a method for interpretation and canonicalization of tables, which are contained in specifications used in the construction industry. [PERSON] et al. [12] introduced a hierarchical clustering algorithm to group the words in a table according to the closeness of their beginning and end positions. [PERSON] et al. [13] developed domain rules for functional and structural analysis of tables. [PERSON] and [PERSON] [14] also proposed a rule-based approach for extracting logical structures from HTML tables and transforming them into an XML representation. [PERSON] et al. [10] addressed the creation of ontologies from tables in a web page. They suggested heuristic rules to create ontologies based on WordNet and domain ontologies. [PERSON] et al. [15] used domain ontology to understand tables in web pages. [PERSON] et al. [16] also used domain ontology to understand structure of tables in web pages, but it is limited to structural analysis of tables. [PERSON] [5,6] used a rule engine to convert unstructured tabular data to a structured form of databases. Domain-specific natural language information and domain-independent graphical information were used to functional and structural analysis of tables. [PERSON] and [PERSON] [17] used CRF (Conditional Random Field) and classification algorithms to find labels and their hierarchical structure in spreadsheets on web pages. [PERSON] et al. [18] studied the patterns that commonly occur in table layouts based on observation from a variety of tables in public table dataset and described automatic table processing systems. Recently, deep learning and word embeddings are used to learn a real-valued vector representation for tables in different Natural Language Processing tasks. [PERSON] et al. [8] adopted deep learning algorithms for table functional and structural analysis. [PERSON] et al. [19] proposed a hybrid deep neural network architecture combining an RNN (Recurrent Neural Network) and CNN (Convolutional Neural Network) for table type classification, and [PERSON] et al. [20] proposed a semantic annotation framework combining knowledge lookup and machine learning to predict a column type. The existing approaches for table understanding described above were based on different methods, e.g., heuristic, probabilistic, dynamic programming, or machine learning methods. They used different assumptions about table structures to reduce the complexity of their own tasks. Those assumptions were embedded in their algorithms and restricted types of tables that could be processed. The previous works focused on tables in a pre-defined form, or tables in HTML documents or financial documents. They did not consider the characteristics of tables in engineering specification. Structure of tables in engineering documents is relatively simple compared to the structure of financial statistical tables, but location of labels and relationships between labels and data are relatively diverse. It means the structures of tables in engineering documents could be different for every table in an engineering document. The variations of table structures make it hard to create a set of rules for different structures so that existing rule-based approaches were limited to do structural tables analysis in engineering specifications. Machine learning approaches like [PERSON] et al. [8] are necessary for functional and structural table analysis in engineering documents. A deep learning algorithm requires