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.

We present an approach for the detection of coordinateterm relationships between entities from the software domain, that refer to Java classes. Usually, relations are found by examining corpus statistics associated with text entities. In some technical domains, however, we have access to additional information about the real-world objects named by the entities, suggesting that coupling information about the “grounded” entities with corpus statistics might lead to improved methods for relation discovery. To this end, we develop a similarity measure for Java classes using distributional information about how they are used in software, which we combine with corpus statistics on the distribution of contexts in which the classes appear in text. Using our approach, cross-validation accuracy on this dataset can be improved dramatically, from around 60% to 88%. Human labeling results show that our classifier has an F1 score of 86% over the top 1000 predicted pairs.
Grounded Discovery of Coordinate Term Relationships between Software Entities

Passengers’ experience is becoming a key metric to evaluate the air transportation system’s performance. Efficient and robust tools to handle airport operations are needed along with a better understanding of passengers’ interests and concerns. Among various airport operations, this paper studies airport gate scheduling for improved passengers’ experience. Three objectives accounting for passengers, aircraft, and operation are presented. Trade-offs between these objectives are analyzed, and a balancing objective function is proposed. The results show that the balanced objective can improve the efficiency of traffic flow in passenger terminals and on ramps, as well as the robustness of gate operations.
Airport Gate Scheduling for Passengers, Aircraft, and Operation

Chemical-chemical interaction (CCI) plays a key role in predicting candidate drugs, toxicity, therapeutic eects, and biological functions. CCI was created from text mining, experiments, similarities, and databases; to date, no learning-based CCI prediction method exist. In chemical analyses, computational approaches are required. e recent remarkable growth and outstanding performance of deep learning have aracted considerable research aention. However, even in state-of-the-art drug analyses, deep learning continues to be used only as a classier. Nevertheless, its purpose includes not only simple classication, but also automated feature extraction. In this paper, we propose the rst end-to-end learning method for CCI, named DeepCCI. Hidden features are derived from a simplied molecular input line entry system (SMILES), which is a string notation representing the chemical structure, instead of learning from craed features. To discover hidden representations for the SMILES strings, we use convolutional neural networks (CNNs). To guarantee the commutative property for homogeneous interaction, we apply model sharing and hidden representation merging techniques. e performance of DeepCCI was compared with a plain deep classier and conventional machine learning methods. e proposed DeepCCI showed the best performance in all seven evaluation metrics used. In addition, the commutative property was experimentally validated. e automatically extracted features through end-to-end SMILES learning alleviates the signicant efforts required for manual feature engineering. It is expected to improve prediction performance, in drug analyses.

DeepCCI: End-to-end Deep Learning for Chemical-Chemical Interaction Prediction