This paper proposes a machine-learning based approach to predict accurately, given a syntactic and semantic context, which preposition is most likely to occur in that context. Each occurrence of a preposition in an English corpus has its context represented by a vector containing 307 features. The vectors are processed by a voted perceptron algorithm to learn associations between contexts and p...

Representation and Prediction Features Initial experiments showed that replacing prepositions by simple place-holders decreases the translation quality. As an extension to the basic approach with plain place-holders, we thus experiment with enriching the place-holders such that they contain more relevant information and represent the content of a preposition while still being in an abstract for...

The place concept has field structure. As in the semantic configuration, nucleus of “space” is highlighted, and remaining components can be interpreted as peripherals. space distinguished by dative ablative cases. case Russian language assumed through prepositions. After prepositions “k”, “po”, “blagodarya” nouns show meaning. using semantically goes to locative meanings provides use these cons...

Prepositions name spatial relationships (e.g., book on a table). But they are also used to convey abstract, non-spatial relationships (e.g., Adrian is on a roll)-raising the question of how the abstract uses relate to the concrete spatial uses. Despite considerable success in delineating these relationships, no general account exists for the two most frequently extended prepositions: in and on....

This paper studies some newly observed phenomena with indefinite descriptions when they appear as complements of prepositional phrases (PPs). We show that in many such cases the indefinite does not receive the traditional existential interpretation, and its quantificational force is sensitive to the identity of the preposition. Such cases of quantificational variability are explained by elabora...

This paper presents a methodology to identify polysemous German prepositions by exploring their vector spatial properties. We apply two cluster evaluation metrics (the Silhouette Value (Kaufman and Rousseeuw, 1990) and a fuzzy version of the V-Measure (Rosenberg and Hirschberg, 2007)) as well as various correlations, to exploit hard vs. soft cluster analyses based on Self-Organising Maps. Our m...

Empirical studies suggest that (a) language is grounded in action and (b) that the immediate past history of use influences how humans produce and comprehend spatial prepositions. We put these assumptions to the test by computationally modelling data from a spatial language task. The task required participants to place functionally related/unrelated objects on a screen w.r.t to a spatial prepos...

We present an unsupervised approach that can be applied to test corrections tasks such as real-word error correction, near-synonym choice, and preposition choice, using n-grams from the Google Web 1T dataset. We present in details the method for correcting preposition errors, which has two phases. We categorize the n-gram types based on the position of the gap that needs to be replaced with a p...

This article describes the application of computational models of spatial prepositions to visually situated dialog systems. In these dialogs, spatial prepositions are important because people often use them to refer to entities in the visual context of a dialog. We first describe a generic architecture for a visually situated dialog system and highlight the interactions between the spatial cogn...

Dealing with prepositions such as “near”, “between”, and “in front of” is very important in Geographic Information Systems (GISs). In most systems, real world distances are used to handle these prepositions. One of the difficulties in processing these prepositions lies in the fact that their geographical range are distorted in people’s cognitive maps. For example, size of an area referred by pr...