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The principle of compositionality is axiomatically assumed in many modern syntacticand semantic research programs, and is integral to the interface of syntax and semantics.Compositionality is the idea that the meaning of a complex linguistic construction generallyought to be derivable based on the meanings of its components and how they are combined.(Frege (1988) Pelletier (1994), [2016], Janssen (2012)) Ellipsis, the systematic inference ofmeaning in the absence of an explicit linguistic string, is a phenomenon of considerableinterest on the syntax-semantics interface precisely because it is one area where the typicalassumptions of compositionality break down. As a result, it is an area where this and otherconstraints of syntactic and semantic theory can be examined and tested. This dissertationarose from a formal research program concerning certain elliptical constructions in English,gaps and strips, which are discussed in more detail and investigated from a variety ofmethodological perspectives throughout the rest of this book.In their 2016 paper, Kubota and Levine put forth an analysis of gapping (G) (1-a) inEnglish in the Type-Logical Grammar (TLG) framework. A subsequent paper, Puthawala(2018) extended this analysis to cover stripping phenomena (S) (1-b). The two analysestaken together license combinations of those constructions such as strip-gaps (SG) (1-c)and gap-strips (GS) (1-d). However examples of these complex constructions, particularlysingle-speaker utterances, are rare and difcult to fnd in corpora, and proved unacceptableto some speakers. These facts gave rise to two questions: First, are strip-gaps and gap-stripswell-formed in English? If evidence suggests that they are, it would validate the predictionsmade by the combined Kubota & Levine and Puthawala (KLP) analysis. If they aren’t,then it provides a straightforward way to challenge the analysis. Supposing that there is reason to believe that strip-gaps and gap-strips are licit for English speakers, then thesecond question then becomes why are they unacceptable to some people?(1) a. Mary picked carrots and Ryan tomatoes.b. Mary picked carrots and Ryan too.c. Mary picked carrots and Ryan too and David tomatoes.d. Mary picked carrots and Ryan tomatoes and David too.In this dissertation, I investigate these constructions through psycholinguistic experiments and the lens of the adaptive control of thought-rational (ACT-R) Parsing framework(Anderson (2005), Lewis and Vasishth (2005), Rasmussen and Schuler (2018) inter alia),which models the domain-general function of cognition and memory and yields output predictions of difculty and reading time. These sentences contain structures and features thatare often taken in the mainstream literature on parsing to be predictive of problems due toconstraints on working memory, predictability, repair, or ambiguity resolution, and I showthat the processing difculty could therefore provide an alternative explanation for the rarity and potential unacceptability of otherwise well-formed constructions, in support of thisexplanation, I develop a parsing algorithm to interface between the competence grammarof TLG and the processing model of ACT-R. This Type-Logical ACT-R parser representsan advancement in formal syntactic rigor over the standardly referenced models in the literature while being compatible with fully parallel models of human sentence processing,surprisal theory to model garden path efects (e.g. Levy (2008)), and neuroanatomicallyplausible models such as distributed associative memory (Rasmussen and Schuler (2018))

Domestic, or household robots, are autonomous robots designed to make our home-life easier by performing chores and mundane tasks such as cleaning, or cooking. Currently domestic robots are specialized to complete a specific task and, therefore, are confined by factors such as mobility, size, and complexity. With the fast development of computer vision and robotics, the need for more compact, advanced and multi-task robots has emerged. Therefore, the robot needs to be multi-functional, able to discern the environment and the tasks. The aim of this paper is to categorize images in domestic robots as relevant to the culinary, laundry, vacuum class or non-relevant at all. The traditional approach in computer vision involves manual annotation of the large number of images and training the model. The most widespread model training techniques comprise of methods such as convolutional neural networks, regression and support vector machine algorithms. We propose an approach that takes a different route by incorporating commonsense knowledge into the algorithm. Our approach, a Commonsense Knowledge-Detector, or a CSK-Detector, performs the basic object detection on a few household objects via Mask R-CNN, and then utilizes commonsense knowledge clauses obtained from a state-of-the-art Knowledge Base “Dice” for large scale image categorization. The live web-camera object detection is also implemented into the model, allowing the CSK-Detector to classify room environment in real time. In addition, our model is a white-box algorithm that returns explainable results in the form of a decision tree. Moreover, it reaches accuracy scores higher than 90% on the whole, which is similar to the black- box core deep learning models in literature. The CSK-Detector Model refinement and expansion to other, non-domestic domains can potentially aid human-robot collaboration and next-generation robotics.