Using Analogy to Model Spatial Language Use and Multimodal Knowledge Capture Kate Lockwood Language and knowledge capture are two skills that allow us to create structured representations of the physical world for reasoning and communication. Spatial prepositions are a form of specialized language that is used to relate two objects in space. In addition to communicating the static location of o...

Many natural languages use prepositions to mark relations between entities of various kinds – between physical entities and their spatial locations, between temporal entities and their temporal locations, between abstract entities of various kinds (e.g. between ideas and their ‘mental locations’). In the current paper I will show that the consequences of using prepositions to relate temporal en...

causal prepositions по and из-зa in russian language are used for theexpression of undesirable or inconvenient facts and conditions and carrynegative charge and connotation. however, their usage requires differentgrammatical structures. moreover, they are used with different nouns.therefore, the sublties invovlved in some similar cases of the usage of theseprepositions for the expression of cau...

The bond between England and the UAE date back to over 220 years ago. This article explored interference of Arabic prepositions in English used United Arab Emirates (UAE), their occurrences light gender level education, two important social variables related linguistic behavior. To do so, participants translated 20 sentences into as well filled 30 gaps with missing prepositions. We also experim...

What factors influence people’s use of spatial prepositions? In this paper, we examine the influence of four factors – geometry of the Ground, function of the Ground, animacy of the Ground, and animacy of the Figure – on the use of English in and on. We find evidence for all four of these factors. We conclude that spatial prepositions appear to involve a complex set of spatial and non-spatial i...

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...

Prepositions and conjunctions are two of the largest remaining bottlenecks in parsing. Across various existing parsers, these two categories have the lowest accuracies, and mistakes made have consequences for downstream applications. Prepositions and conjunctions are often assumed to depend on lexical dependencies for correct resolution. As lexical statistics based on the training set only are ...

Introduction For many ESL learners, English prepositions pose a perennial problem, partly because the same lexical item -e.g., the word over -can have a variety of functions (as preposition, noun, adverb, adjective and particle (in verb+particle constructions)) and meanings, and partly because of its diversity in collocational properties. With no generalizations emerging, a common learning stra...

Prepositions in English are a well-known challenge for language learners, and the computational analysis of preposition usage has attracted significant attention. Such research generally starts out by developing models of preposition usage for native English based on a range of features, from shallow surface evidence to deep linguistically-informed properties. While we agree that ultimately a c...