TASK DEFINITION: In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
PROBLEM: As food becomes an important part of modern life, recipes shared on the web are a great indicator of civilizations and culinary attitudes in different countries. Similarly, ingredients, flavors, and nutrition information are strong signals of the taste preferences of individuals from various parts of the world. Yet, we do not have a thorough understanding of these palate varieties. In this paper, we present a large-scale study of recipes published on the Web and their content, aiming to understand cuisines and culinary habits around the world. Using a database of more than 157K recipes from over 200 different cuisines, we analyze ingredients, flavors, and nutritional values which distinguish dishes from different regions, and use this knowledge to assess the predictability of recipes from different cuisines. We then use country health statistics to understand the relation between these factors and health indicators of different nations, such as obesity, diabetes, migration, and health expenditure. Our results confirm the strong effects of geographical and cultural similarities on recipes, health indicators, and culinary preferences between countries around the world.

SOLUTION: Kissing Cuisines: Exploring Worldwide Culinary Habits on the Web

PROBLEM: Dawid, Kjrerulff & Lauritzen (1994) provided a preliminary description of a hybrid between Monte-Carlo sampling methods and exact lo­ cal computations in junction trees. Utiliz­ ing the strengths of both methods, such hy­ brid inference methods has the potential of expanding the class of problems which can be solved under bounded resources as well as solving problems which otherwise resist ex­ act solutions. The paper provides a detailed description of a particular instance of such a hybrid scheme; namely, combination of ex­ act inference and Gibbs sampling in discrete Bayesian networks. We argue that this com­ bination calls for an extension of the usual message passing scheme of ordinary junction trees.

SOLUTION: HUGS: Combining Exact Inference and Gibbs Sampling in Junction Trees

PROBLEM: We present a general-purpose tagger based on convolutional neural networks (CNN), used for both composing word vectors and encoding context information. The CNN tagger is robust across different tagging tasks: without task-specific tuning of hyper-parameters, it achieves state-of-theart results in part-of-speech tagging, morphological tagging and supertagging. The CNN tagger is also robust against the outof-vocabulary problem, it performs well on artificially unnormalized texts.

SOLUTION:
A General-Purpose Tagger with Convolutional Neural Networks