The Python Papers Anthology
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An Anthological Review of Research Utilizing MontyLingua: a Python-Based End-to-End Text Processor
Get PDFMontyLingua, an integral part of ConceptNet which is currently the largest commonsense knowledge base, is an English text processor developed using Python programming language in MIT Media Lab. The main feature of MontyLingua is the coverage for all aspects of English text processing from raw input text to semantic meanings and summary generation, yet each component in MontyLingua is loosely-coupled to each other at the architectural and code level, which enabled individual components to be used independently or substituted. However, there has been no review exploring the role of MontyLingua in recent research work utilizing it. This paper aims to review the use of and roles played by MontyLingua and its components in research work published in 19 articles between October 2004 and August 2006. We had observed a diversified use of MontyLingua in many different areas, both generic and domain-specific. Although the use of text summarizing component had not been observe, we are optimistic that it will have a crucial role in managing the current trend of information overload in future research
Python Switch Statement
Get PDFThe Python programming language does not have a built in switch/case control structure as found in many other high level programming languages. It is thought by some that this is a deficiency in the language, and the control structure should be added. This paper demonstrates that not only is the control structure not needed, but that the methods available in Python are more expressive than built in case statements in other high level languages
Pyphant - A Python Framework for Modelling Reusable Information Processing Tasks
Get PDFWe are presenting the Python framework Pyphant for the creation and application of information flow models. The central idea of this approach is to encapsulate each data processing step in one unit which we call a worker. A worker receives input via sockets and provides the results of its data processing via plugs. These can be connected to other workers' sockets. The resulting directed graph is called a recipe. Classes for these objects comprise the Pyphant core. To implement the actual processing steps, Pyphant relies on third-party plug-ins which extend the basic worker class and can be distributed as Python eggs. On top of the core, Pyphant offers an information exchange layer which facilitates the interoperability of the workers, using Numpy objects. A third layer comprises textual and graphical user interfaces. The former allows for the batch processing of data and the latter allows for the interactive construction of recipes.This paper discusses the Pyphant framework and presents an example recipe for determining the length scale of aggregated polymeric phases, building an amphiphilic conetwork from an Atomic Force Microscopy (AFM) phase mode image