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: Abstract: We often encounter situations in which an experimenter wants to find, by sequential experimentation, xmax = arg maxx f(x), where f(x) is a (possibly unknown) function of a well controllable variable x. Taking inspiration from physics and engineering, we have designed a new method to address this problem. In this paper, we first introduce the method in continuous time, and then present two algorithms for use in sequential experiments. Through a series of simulation studies, we show that the method is effective for finding maxima of unknown functions by experimentation, even when the maximum of the functions drifts or when the signal to noise ratio is low.

Output: Thompson sampling with the online bootstrap


Input: Consider Input: We propose a novel fully-automated approach towards inducing multilingual taxonomies from Wikipedia. Given an English taxonomy, our approach first leverages the interlanguage links of Wikipedia to automatically construct training datasets for the is-a relation in the target language. Character-level classifiers are trained on the constructed datasets, and used in an optimal path discovery framework to induce high-precision, high-coverage taxonomies in other languages. Through experiments, we demonstrate that our approach significantly outperforms the state-of-the-art, heuristics-heavy approaches for six languages. As a consequence of our work, we release presumably the largest and the most accurate multilingual taxonomic resource spanning over 280 languages.

Output: 280 Birds with One Stone: Inducing Multilingual Taxonomies from Wikipedia Using Character-level Classification


Input: Consider Input: A rhetorical structure tree (RS tree) is a representation of discourse relations among elementary discourse units (EDUs). A RS tree is very useful to many text processing tasks employing relationships among EDUs such as text understanding, summarization, and question-answering. Thai language with its unique linguistic characteristics requires a unique RS tree construction technique. This paper proposes an approach for Thai RS tree construction which consists of three major steps: EDU segmentation, Thai RS tree construction, and discourse relation (DR) identification. Two hidden markov models derived from grammatical rules are used to segment EDUs, a clustering technique with its similarity measure derived from Thai semantic rules is used to construct a Thai RS tree, and a decision tree whose features extracted from the rules is used to determine the DR between EDUs. The proposed technique is evaluated using three Thai corpora. The results show the Thai RS tree construction and the DR identification effectiveness of 94.90% and 82.81%, respectively. KeywordsThai Language, Element Discourse Unit, Rhetorical Structure Tree, Discourse Relation.


Output: THAI RHETORICAL STRUCTURE ANALYSIS