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Our recent papers...

posted Jun 23, 2016, 12:03 AM by Alberto Bartoli   [ updated Jun 24, 2016, 2:07 AM by Eric Medvet ]
Very good news for the lab: three papers just accepted at prestigious conferences!

These papers focus on very different application domains and propose approaches based on very different machine learning paradigms.

Syntactical Similarity Learning by means of Grammatical Evolution
  • Several research efforts have shown that a similarity function synthesized from examples may capture an application-specific similarity criterion in a way that fits the application needs more effectively than a generic distance definition.
  • We propose a similarity learning algorithm tailored to problems of syntax-based entity extraction from unstructured text streams.
  • The algorithm takes in input pairs of strings along with a binary indication of whether they adhere or not adhere to the same syntactic pattern.
  • Our approach is based on Grammatical Evolution and learns a function expressed with a specialized, simple language that we have defined for this purpose.
  • (abstract)
A Language and an Inference Engine for Twitter Filtering Rules
  • We consider the problem of the filtering of Twitter posts, that is, the hiding of those posts which the user prefers not to visualize on his/her timeline.
  • We define a language for specifying filtering policies suitable for Twitter posts. The language allows each user to decide which posts to filter out based on his/her sensibility and preferences.
  • We also propose a method for inferring a policy automatically, based solely on examples of the desired filtering behavior.
  • The method is based on an evolutionary approach driven by a multi-objective optimization scheme.
  • (abstract)
"Best Dinner Ever!!!": Automatic Generation of Restaurant Reviews with LSTM-RNN
  • Consumer reviews are an important information resource for people and a fundamental part of everyday decision-making.
  • We investigate the possibility of generating hundreds of false restaurant reviews automatically and very quickly.
  • We propose and evaluate a method for automatic generation of restaurant reviews tailored to the desired rating and restaurant category.
  • Our method is based on a "long-short term memory" recurrent neural network.
  • As it turns out, it is feasible to automatically generate realistic reviews which can manipulate the opinion of the user.
  • (abstract)