On the Quantum-like Contextuality of Ambiguous Phrases

Language is contextual as meanings of words are dependent on their contexts. Contextuality is, concomitantly, a well-defined concept in quantum mechanics where it is considered a major resource for quantum computations. We investigate whether natural language exhibits any of the quantum mechanics' contextual features. We show that meaning combinations in ambiguous phrases can be modelled in the sheaf-theoretic framework for quantum contextuality, where they can become possibilistically contextual. Using the framework of Contextuality-by-Default (CbD), we explore the probabilistic variants of these and show that CbD-contextuality is also possible.

Automatic Classification of Attributes in German Adjective-Noun Phrases

Adjectives such as heavy (as in heavy rain) and windy (as in windy day) provide possible values for the attributes intensity and climate, respectively. The attributes themselves are not overtly realized and are in this sense implicit. While these attributes can be easily inferred by humans, their automatic classification poses a challenging task for computational models. We present the following contributions: (1) We gain new insights into the attribute selection task for German. More specifically, we develop computational models for this task that are able to generalize to unseen data. Moreover, we show that classification accuracy depends, inter alia, on the degree of polysemy of the lexemes involved, on the generalization potential of the training data and on the degree of semantic transparency of the adjective-noun pairs in question. (2) We provide the first resource for computational and linguistic experiments with German adjective-noun pairs that can be used for attribute selection and related tasks. In order to safeguard against unwelcome memorization effects, we present an automatic data augmentation method based on a lexical resource that can increase the size of the training data to a large extent.

Building hydrological model for designing culverts using GIS Technique

Hydrological studies in Syria- in general- based on classical methods to derive catchment areas of road culverts or estimating the design storm for these catchments. Such operations have a significant degree of complexity, effort and time costing, up to implementing clear concept about streams crossing the road and deriving the catchments boundaries to estimate the designing storm discharge according to road classification and passing facility type preventing road inundation or demolition. In this study the GIS technique was used to derive catchments for all outlets on this road and the morphological properties were estimated. The developed hydrological model for studied road allows designers to evaluate more accurate solutions in such engineering cases like determiningthe best locations for culverts on roads, estimating design storm and design discharge for those outlets and all other engineering characteristics “designing parameters” such as longitudinal slope, span, area, number of openings. as it allows the development of various economic solutions in order to choose the best one from both engineering and economic view points, according to road classification and storm probability.