Government accounting optimization based on computational linguistics

The level of moral development and moral intensity in cognitive psychology will not only affect the ethical behavior of accountants, but also have a direct impact on the quality and level of accounting work. Therefore, in this paper, the ethical behavior of accountants was analyzed from the perspective of cognitive psychology. Computer-aided data mining techniques were introduced, and government accounting risk assessment management of financial accountants was studied. In this paper, the principle of cognitive psychology to measure the ethical level of accountants was first described. The predicament of moral judgments was analyzed and an optimization plan to improve the ethical intention of accountants was proposed. Support Vector Machine classification technology in data mining was studied to explore how to conduct effective and reliable evaluation, so as to provide a scientific basis for decision-making in improving accounting management. After the simulation experiment, it is proved that continuously improving the ethical standards of accountants and strengthening the forecast of accounting risks can continue to optimize the accounting office management.     

Statistical models for the induction and use of selectional preferences

Selectional preferences have a long history in both generative and computational linguistics. However, since the publication of Resnik's dissertation in 1993, a new approach has surfaced in the computational linguistics community. This new line of research combines knowledge represented in a pre‐defined semantic class hierarchy with statistical tools including information theory, statistical modeling, and Bayesian inference. These tools are used to learn selectional preferences from examples in a corpus. Instead of simple sets of semantic classes, selectional preferences are viewed as probability distributions over various entities. We survey research that extends Resnik's initial work, discuss the strengths and weaknesses of each approach, and show how they together form a cohesive line of research.     

Pinning down polysemy: A formalisation for a Brazilian Portuguese preposition

This paper presents a formal definition for a number of distinct cases where the preposition “em” (in) can be applied in Brazilian Portuguese. The aim of this work is to establish a mathematical model using qualitative spatial reasoning formalisms within the idea of precisification from supervaluation semantics. Our long term goal is to implement this model into artificial intelligent systems to allow for seamless communication with humans in a common speech.     

Meaningfulness Beats Frequency in Multiword Chunk Processing

Whereas a growing bulk of work has demonstrated that both adults and children are sensitive to frequently occurring word sequences, little is known about the potential role of meaning in the processing of such multiword chunks. Here, we take a first step toward assessing the contribution of meaningfulness in the processing of multiword sequences, using items that varied in chunk meaningfulness. In a phrasal-decision study, we compared reaction times for triads of three-word sequences, corresponding to idiomatic expressions, compositional phrases, and phrasal fragments, while controlling for phrase and substring frequencies. Chunk meaningfulness, as assessed by a separate subjective rating study, was found to speed up decision times for all three types of strings: The more meaningful a multiword sequence was judged to be, the faster it was processed, independently of whether it was idiomatic, compositional in nature, or a phrasal fragment. These results highlight the importance of taking meaning into account when considering the processing of multiword chunks, consistent with predictions of construction-based approaches to language.     

Rectifying the Mischaracterization of Logic by Mental Model Theorists

Khemlani et al. (2018) mischaracterize logic in the course of seeking to show that mental model theory (MMT) can accommodate a form of inference (, let us label it) they find in a high percentage of their subjects. We reveal their mischaracterization and, in so doing, lay a landscape for future modeling by cognitive scientists who may wonder whether human reasoning is consistent with, or perhaps even capturable by, reasoning in a logic or family thereof. Along the way, we note that the properties touted by Khemlani et al. as innovative aspects of MMT-based modeling (e.g., nonmonotonicity) have for decades been, in logic, acknowledged and rigorously specified by families of (implemented) logics. Khemlani et al. (2018) further declare that is “invalid in any modal logic.” We demonstrate this to be false by our introduction (Appendix A) of a new propositional modal logic (within a family of such logics) in which is provably valid, and by the implementation of this logic. A second appendix, B, partially answers the two-part question, “What is a formal logic, and what is it for one to capture empirical phenomena?”