The paper outlines an account of how the brain might process questions and answers in linguistic interaction, focusing on accessing answers in memory and combining questions and answers into propositions. To enable this, we provide an approximation of the lambda calculus implemented in the Semantic Pointer Architecture (SPA), a neural implementation of a Vector Symbolic Architecture. The account builds a bridge between the type-based accounts of propositions in memory (as in the treatments of belief by Ranta (1994) and Cooper (2023) and the suggestion for question answering made by Eliasmith (2013) question answering is described in terms of transformations of structured representations in memory providing an answer. We will take such representations to correspond to beliefs of the agent. On Cooper’s analysis, beliefs are considered to be types which have a record structure closely related to the structure which Eliasmith codes in vector representations (Larsson et al, 2023). Thus the act of answering a question can be seen to have a neural base in a vector transformation translatable in Eliasmith’s system to activity of spiking neurons and to correspond to using an item in memory (abelief) to provide an answer to the question.

The paper extends a referentially transparent approach which has been successfully applied to the analysis of declarative quantified NPs to wh-phrases. This uses data from dialogical phenomena such as clarification interaction, anaphora, and incrementality as a guide to the design of wh-phrase meanings.

We argue that mainly due to technical innovation in the landscape of annotation tools, a conceptual change in annotation models and processes is also on the horizon. It is diagnosed that these changes are bound up with multi-media and multi-perspective facilities of annotation tools, in particular when considering virtual reality (VR) and augmented reality (AR) applications, their potential ubiquitous use, and the exploitation of externally trained natural language pre-processing methods. Such developments potentially lead to a dynamic and exploratory heuristic construction of the annotation process. With TextAnnotator an annotation suite is introduced which focuses on multi-mediality and multi-perspectivity with an interoperable set of task-specific annotation modules (e.g., for word classification, rhetorical structures, dependency trees, semantic roles, and more) and their linkage to VR and mobile implementations. The basic architecture and usage of TextAnnotator is described and related to the above mentioned shifts in the field.

Much work in contemporary computational semantics follows the distributional hypothesis (DH), which is understood as an approach to semantics according to which the meaning of a word is a function of its distribution over contexts which is represented as vectors (word embeddings) within a multi-dimensional semantic space. In practice, use is identified with occurrence in text corpora, though there are some efforts to use corpora containing multi-modal information. In this paper we argue that the distributional hypothesis is intrinsically misguided as a self-supporting basis for semantics, as Firth was entirely aware. We mention philosophical arguments concerning the lack of normativity within DH data. Furthermore, we point out the shortcomings of DH as a model of learning, by discussing a variety of linguistic classes that cannot be learnt on a distributional basis, including indexicals, proper names, and wh-phrases. Instead of pursuing DH, we sketch an account of the problematic learning cases by integrating a rich, Firthian notion of dialogue context with interactive learning in signalling games backed by in probabilistic Type Theory with Records. We conclude that the success of the DH in computational semantics rests on a post hoc effect: DS presupposes a referential semantics on the basis of which utterances can be produced, comprehended and analysed in the first place.