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.

Input: Consider Input: Existing algorithms for subgroup discovery with numerical targets do not optimize the error or target variable dispersion of the groups they find. This often leads to unreliable or inconsistent statements about the data, rendering practical applications, especially in scientific domains, futile. Therefore, we here extend the optimistic estimator framework for optimal subgroup discovery to a new class of objective functions: we show how tight estimators can be computed efficiently for all functions that are determined by subgroup size (non-decreasing dependence), the subgroup median value, and a dispersion measure around the median (nonincreasing dependence). In the important special case when dispersion is measured using the mean absolute deviation from the median, this novel approach yields a linear time algorithm. Empirical evaluation on a wide range of datasets shows that, when used within branch-and-bound search, this approach is highly efficient and indeed discovers subgroups with much smaller errors.

Output: Identifying Consistent Statements about Numerical Data with Dispersion-Corrected Subgroup Discovery


Input: Consider Input: Rapid crisis response requires real-time analysis of messages. After a disaster happens, volunteers attempt to classify tweets to determine needs, e.g., supplies, infrastructure damage, etc. Given labeled data, supervised machine learning can help classify these messages. Scarcity of labeled data causes poor performance in machine training. Can we reuse old tweets to train classifiers? How can we choose labeled tweets for training? Specifically, we study the usefulness of labeled data of past events. Do labeled tweets in different language help? We observe the performance of our classifiers trained using different combinations of training sets obtained from past disasters. We perform extensive experimentation on real crisis datasets and show that the past labels are useful when both source and target events are of the same type (e.g. both earthquakes). For similar languages (e.g., Italian and Spanish), cross-language domain adaptation was useful, however, when for different languages (e.g., Italian and English), the performance decreased.

Output: Cross-Language Domain Adaptation for Classifying Crisis-Related Short Messages


Input: Consider Input: Embedding words in a vector space has gained a lot of research attention in re-<lb>cent years. While state-of-the-art methods provide efficient computation of word<lb>similarities via a low-dimensional matrix embedding, their motivation is often left<lb>unclear. In this paper, we argue that word embedding can be naturally viewed<lb>as a ranking problem. Then, based on this insight, we propose a novel frame-<lb>work WordRank that efficiently estimates word representations via robust ranking.<lb>The performance of WordRank is measured in word similarity and word analogy<lb>benchmarks, and the results are compared to the state-of-the-art word embedding<lb>techniques. Our algorithm produces a vector space with meaningful substructure,<lb>as evidenced by its performance of 77.4% accuracy on a popular word similarity<lb>benchmark and 76% on the Google word analogy benchmark. WordRank per-<lb>forms especially well on small corpora.


Output: WordRank: Learning Word Embeddings via Robust Ranking