Векторлық кеңістік модельдері

Vector space models of word meaning typi- cally represent the meaning of a word as a vec- tor computed by summing over all its corpus occurrences. Words close to this …...

AbstractVector space models benefit from using an outside corpus to train the model. It is, however, unclear what constitutes a good training corpus. We have investigated the effect on summary …...

In most previous research on distribu- tional semantics, Vector Space Models (VSMs) of words are built either from topical information (e.g., documents in which a word is present), or from …...

This paper explores two hypotheses regarding vector space models that predict the compo- sitionality of German noun-noun compounds: (1) Against our intuition, we demonstrate that window-based rather than syntax-based distri- …...

Generative probabilistic models have been used for content modelling and template induction, and are typically trained on small corpora in the target domain. In contrast, vector space models of distribu- …...

We present a novel approach to the problem of multilingual conceptual metaphor recognition. Our approach extends recent work in conceptual metaphor discovery by combining a complex methodology for facet-based concept …...

Structured distributional semantic models aim to improve upon simple vector space models of semantics by hypothesizing that the meaning of a word is captured more effectively through its relational — …...

Vector space models (VSMs) represent word meanings as points in a high dimen- sional space. VSMs are typically created using a large text corpora, and so repre- sent word semantics …...

Vector-space models derived from corpora are an effective way to learn a representa- tion of word meaning directly from data, and these models have many uses in prac- tical applications. …...

AbstractThe paper presents an iterative bidirectional clustering of adjectives and nouns based on a co-occurrence matrix. The clustering method combines a Vector Space Models (VSM) and the results of a …...

AbstractVector space models and distributional information are widely used in NLP. The models typically rely on complex, high-dimensional objects. We present an interactive visualisation tool to explore salient lexical-semantic features …...

AbstractThis paper explores the possibilities of analogical reasoning with vector space models. Given two pairs of words with the same relation (e.g. man:woman :: king:queen), it was proposed that the …...

AbstractVector space models of words have long been claimed to capture linguistic regularities as simple vector translations, but problems have been raised with this claim. We decompose and empirically analyze …...