Interpretable semantic vectors from a joint model of brain-and text-based meaning

Vector space models (VSMs) represent word meanings as points in a high dimensional space. VSMs are typically created using a large text corpora, and so represent word semantics as observed in text. We present a new algorithm (JNNSE) that can incorporate a measure of semantics not previously used to create VSMs: brain activation data recorded while people read words. The resulting model takes advantage of the complementary strengths and weaknesses of corpus and brain activation data to give a more complete representation of semantics. Evaluations show that the model 1) matches a behavioral measure of semantics more closely, 2) can be used to predict corpus data for unseen words and 3) has predictive power that generalizes across brain imaging technologies and across subjects. We believe that the model is thus a more faithful representation of mental vocabularies.

Custom coprocessor based matrix algorithms for image and signal processing

Matrix algorithms are important in many types of applications including image and signal processing. A close examination of the algorithms used in these, and related, applications reveals that many of the fundamental actions involve matrix algorithms such as matrix multiplication. This paper presents an investigation into the design and implementation of different matrix algorithms such as matrix operations, matrix transforms and matrix decompositions using a novel custom coprocessor system for MATrix algorithms based on Reconfigurable Computing (RCMAT). The proposed RCMAT architectures are scalable, modular and require less area and time complexity with reduced latency when compared with existing structures.

Discourse and Text

This article discusses tense and aspect in the context of attested forms of discourse and text. The emphasis is on the semantic, pragmatic, textual, and stylistic functions of tense in context, taking into account linguistic features in the surrounding discourse, as well as the importance of factors such as medium (spoken or written), register (degree of formality), text type (literary vs. journalistic vs. conversational etc.), and discourse mode (narrative vs. report vs. description, etc.). Thus, tense and aspect are analyzed not purely as part of a linguistic “system” as such, but in the context of particular texts or forms of discourse. The article also explores the concept of “markedness” through two case studies: the narrative present and the narrative imperfect. Finally, it assesses the roles played by tenses in conveying particular points of view in texts, including shifts and/or ambiguities in point of view; Segmented Discourse Representation Theory; internal focalization and the French imperfective past tense; and textual polyphony.