Computers understand very little of the meaning of human language. This profoundly limits our ability to give instructions to computers, the ability of computers to explain their actions to us, and the ability of computers to analyse and process text. Vector space models (VSMs) of semantics

hypothesis is valid and can be successfully implemented. Second, it explores different types of semantic VSMs, outlining which one is more suitable for representing a frame semantic resource. In the paper, VSMs are used for modeling the linguistic core of a frame, the lexical units. Indeed, if the hypothesis

hypothesis is valid and can be successfully implemented. Second, it explores different types of semantic VSMs, outlining which one is more suitable for representing a frame semantic resource. In the paper, VSMs are used for modeling the linguistic core of a frame, the lexical units. Indeed, if the hypothesis

Abstract 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

In most previous research on distributional semantics, Vector Space Models (VSMs) of words are built either from topical information (e.g., documents in which a word is present), or from syntactic/semantic types of words (e.g., dependency parse links of a word in sentences), but not both

Vector space models (VSMs) are math-ematically well-defined frameworks that have been widely used in the distributional approaches to semantics. In VSMs, high-dimensional vectors represent linguistic entities. In an application, the similar-ity of vectors—and thus the entities that they represent

to help enhancing IR, but the semantic analyzer often fails or needs a large amount of training data. We propose a hybrid model which combines dedicated VSMs for concept slots with a general VSM as a backoff. The model has been evaluated in a book search task and shown to be effective and robust against

Abstract. Vector space models (VSMs) are often employed as mathematical representations of documents for tasks like indexing, information retrieval (IR), filtering and others, where documents are represented as vectors of their index terms or keywords. In a vector space representation