Encoding Sequential Information in Vector Space Models of Semantics: Comparing Holographic Reduced Representation and Random Permutation

Encoding information about the order in which words typically appear has been shown to improve the performance of high-dimensional semantic space models. This requires an encoding operation capable of binding together vectors in an order-sensitive way, and efficient enough to scale to large text corpora. Although both circular convolution and random permutations have been enlisted for this purpose in semantic models, these operations have never been systematically compared. In Experiment 1 we compare their storage capacity and probability of correct retrieval; in Experiments 2 and 3 we compare their performance on semantic tasks when integrated into existing models. We conclude that random permutations are a scalable alternative to circular convolution with several desirable properties

The polysemy of the Spanish verb sentir: a behavioral profile analysis

This study investigates the intricate polysemy of the Spanish perception verb sentir (‘feel’) which, analogous to the more-studied visual perception verbs ver (‘see’) and mirar (‘look’), also displays an ample gamut of semantic uses in various syntactic environments. The investigation is based on a corpus-based behavioral profile (BP) analysis. Besides its methodological merits as a quantitative, systematic and verifiable approach to the study of meaning and to polysemy in particular, the BP analysis offers qualitative usage-based evidence for cognitive linguistic theorizing. With regard to the polysemy of sentir, the following questions were addressed: (1) What is the prototype of each cluster of senses? (2) How are the different senses structured: how many senses should be distinguished – i.e. which senses cluster together and which senses should be kept separately? (3) Which senses are more related to each other and which are highly distinguishable? (4) What morphosyntactic variables make them more or less distinguishable? The results show that two significant meaning clusters can be distinguished, which coincide with the division between the middle voice uses (sentirse) and the other uses (sentir). Within these clusters, a number of meaningful subclusters emerge, which seem to coincide largely with the more general semantic categories of physical, cognitive and emotional perception

An application of the FIS-CRM model to the FISS metasearcher: Using fuzzy synonymy and fuzzy generality for representing concepts in documents

AbstractThe main objective of this work is to improve the quality of the results produced by the Internet search engines. In order to achieve it, the FIS-CRM model (Fuzzy Interrelations and Synonymy based Concept Representation Model) is proposed as a mechanism for representing the concepts (not only terms) contained in any kind of document. This model, based on the vector space model, incorporates a fuzzy readjustment process of the term weights of each document. The readjustment lies on the study of two types of fuzzy interrelations between terms: the fuzzy synonymy interrelation and the fuzzy generality interrelations (“broader than” and “narrower than” interrelations). The model has been implemented in the FISS metasearcher (Fuzzy Interrelations and Synonymy based Searcher) that, using a soft-clustering algorithm (based on the SISC algorithm), dynamically produces a hierarchical structure of groups of “conceptually related” documents (snippets of web pages, in this case)