TDL : a type description language for HPSG. - Part 1: Overview

Unification-based grammar formalisms have become the predominant paradigm in natural language processing NLP and computational linguistics CL. Their success stems from the fact that they can be seen as high-level declarative programming languages for linguists, which allow them to express linguistic knowledge in a monotonic fashion. More over, such formalisms can be given a precise set theoretical semantics. This paper presents mathcal{TDL}, a typed featurebased language and inference system, which is specically designed to support highly lexicalized grammar theories like HPSG, FUG, or CUG. mathcal{TDL} allows the user to define possibly recursive hierarchically ordered types consisting of type constraints and feature constraints over the boolean connectives wedge, vee, and neg. mathcal{TDL} distinguishes between avm types (open-world reasoning), sort types (closed-world reasoning), built-in types and atoms, and allows the declaration of partitions and incompatible types. Working with partially as well as with fully expanded types is possible, both at definition time and at run time. mathcal{TDL} is incremental, i.e., it allows the redefinition of types and the use of undefined types. Efficient reasoning is accomplished through four specialized reasoners

Parameterized type expansion in the feature structure formalism TDL

Over the last few years, unification-based grammar formalisms have become the predominant paradigm in natural language processing systems because of their monotonicity, declarativeness, and reversibility. From the viewpoint of computer science, typed feature structures can be seen as data structures that allow representation of linguistic knowledge in a uniform fashion. Type expansion is an operation that makes the constraints on a typed feature structure explicit and determines their satisfiability. We describe an efficient expansion algorithm that takes care of recursive type definitions and allows exploration of different expansion strategies through the use of control knowledge. This knowledge is specified in a separate layer, independently of grammatical information. Memoization of the type expansion function drastically reduces the number of unifications. In the second part, nonmonotonic extensions to TDL and the implementation of well-typedness checks are presented. Both are closely related to the type expansion algorithm. The algorithms have been implemented in Common Lisp and are integrated parts of TDL and a large natural language dialog system

Automated code compliance checking in the construction domain using semantic natural language processing and logic-based reasoning

Construction projects must comply with various regulations. The manual process of checking the compliance with regulations is costly, time consuming, and error prone. With the advancement in computing technology, there have been many research efforts in automating the compliance checking process, and many software development efforts led by industry bodies/associations, software companies, and/or government organizations to develop automated compliance checking (ACC) systems. However, two main gaps in the existing ACC efforts are: (1) manual effort is needed for extracting requirements from regulatory documents and encoding these requirements in a computer-processable rule format; and (2) there is a lack of a semantic representation for supporting automated compliance reasoning that is non-proprietary, non-hidden, and user-understandable and testable. To address these gaps, this thesis proposes a new ACC method that: (1) utilizes semantic natural language processing (NLP) techniques to automatically extract regulatory information from building codes and design information from building information models (BIMs); and (2) utilizes a semantic logic-based representation to represent and reason about the extracted regulatory information and design information for compliance checking. The proposed method is composed of four main methods/algorithms that are combined in one computational framework: (1) a semantic, rule-based method and algorithm that leverage NLP techniques to automatically extract regulatory information from building codes and represent the extracted information into semantic tuples, (2) a semantic, rule-based method and algorithm that leverage NLP techniques to automatically transform the extracted regulatory information into logic rules to prepare for automated reasoning, (3) a semantic, rule-based information extraction and information transformation method and algorithm to automatically extract design information from BIMs and transform the extracted information into logic facts to prepare for automated reasoning, and (4) a logic-based information representation and compliance reasoning schema to represent regulatory and design information for enabling the automated compliance reasoning process. To test the proposed method, a building information model test case was developed based on the Duplex Apartment Project from buildingSMARTalliance of the National Institute of Building Sciences. The test case was checked for compliance with a randomly selected chapter, Chapter 19, of the International Building Code 2009. Comparing to a manually developed gold standard, 87.6% precision and 98.7% recall in noncompliance detection were achieved, on the testing data