Duplicate Question Management and Answer Verification System Management of large data sets of question papers can be cumbersome, especially when dealing with potential duplicate or erroneous questions. The addition of a natural language system that automatically handles these issues would greatly speed up the verification of such data sets. This is a tool for identifying semantic similarity between sentences in plaintext English. Handpicked features were selected which included simple structural features and word embedding features using word2vec with multiple distance metrics between the resulting sentence vectors. The model is trained on weak hardware allowing for sufficiently high accuracy on low end machines. Results demonstrate the effectiveness of boosting for improving the performance of simple learning models, allowing for complex learning in the absence of high end hardware.

Datadriven construction of SPARQL queries by approximate question graph alignment in question answering over knowledge graphs Abstract   As increasingly more semantic realworld data is stored in knowledge graphs, providing intuitive and effective query methods for endusers is a fundamental and challenging task. Since there is a gap between the plain natural language question (NLQ) and structured data, most RDF questionanswering (QA) systems construct SPARQL queries from NLQs and obtain precise answers from knowledge graphs. A major challenge is how to disambiguate the mapping of phrases and relations in a question to the dataset items, especially in complex questions. In this paper, we propose a novel datadriven graph similarity framework for RDF QA to extract the query graph patterns directly from the knowledge graph instead of constructing them with semantically mapped items. An uncertain question graph is presented to model the interpretations of an NLQ, based on which our problem is reduced to a graph alignment problem. In formulating the alignment, both the lexical and structural similarity of graphs are considered, hence, the target RDF subgraph is used as a query graph pattern to construct the final query. We create a pruned entity graph dynamically based on the complexity of an input question to reduce the search space on the knowledge graph. Moreover, to reduce the calculating cost of the graph similarity, we compute the similarity scores only for samedistance graph elements and equip the process with an edge associationaware surface form extraction method. Empirical studies over real datasets indicate that our proposed approach is flexible and effective as it outperforms stateoftheart methods significantly.