Software & Demos
This is a Java based wrapper over Stanford's NLP POS Tagger (English only). It reads the contents of the user specified input file (line by line) and prints out the parsed text in the following format: "that/DT has/VBZ never/RB happened/VBN before/RB ./.". More instructions in the readme. Example usage:
java -Xmx1G -Xms1G -jar Postag1.0.jar "<PATH TO INPUT FILE>" > parsed_output.txt
This is a perl script that takes in all your system generated files, then all your gold standard/reference summary files, and prepares it in the format used by the ROUGE evaluation toolkit. In other words, it prepares the following from the input that you provide:
There are 2 versions of the script, one is output using jackknifing and the other is the usual evaluation without the need for jackknifing.
Traditional web search engines enable users to find documents based on topics. However, in finding entities such as restaurants, hotels and products, traditional search engines fail to suffice as users are often interested in finding entities based on structured attributes such as price and brand and unstructured information such as opinions of other web users. In this paper, we showcase a preference driven search system, that enables users to find entities of interest based on a set of structured preferences as well as unstructured opinion preferences.
FindiLike is a preference driven search engine that enables people to find and analyze entities (currently only hotels) based on personal preferences. These preferences can be a combination of unstructured information such as opinions or structured ones such as price, distance and so on depending on the domain. The opinion preferences can be expressed as a set of natural keywords such as 'close to place xyz', 'safe location', 'good breakfast', etc. FindiLike also provides tools to further analyze the recommended entities.