Call-For-Papers Retrieval System based on Active Learning and Semantic Similarity

Abstract

Choosing an appropriate conference or symposium is the key in publishing a scientific work. We propose a Call-For-Papers Retrieval System (CRS) which provides a list of relevant calls for papers given the textual content of a paper as the input query. The core of the system is a binary classification model trained by the user feedback collected in a continuous active learning strategy. It allows the user to leverage term weighting strategy to emphasize keyterms of the query. Retrievals are initially made based on a modified Bag of Word (BoW) representation which introduces the WordNet Synset to enrich its semantic background. Then a semantic ranking module prioritizes the retrievals using semantic similarity algorithm. The final ranked list is generated by merging the intermediate lists and then displayed to the user for labeling. The user submits the feedback to update result iteratively or terminate the iteration directly.