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.

[Q]: A crucial aspect of a knowledge base population system that extracts new facts from text corpora, is the generation of training data for its relation extractors. In this paper, we present a method that maximizes the effectiveness of newly trained relation extractors at a minimal annotation cost. Manual labeling can be significantly reduced by Distant Supervision, which is a method to construct training data automatically by aligning a large text corpus with an existing knowledge base of known facts. For example, all sentences mentioning both ‘Barack Obama’ and ‘US’ may serve as positive training instances for the relation born in(subject,object). However, distant supervision typically results in a highly noisy training set: many training sentences do not really express the intended relation. We propose to combine distant supervision with minimal manual supervision in a technique called feature labeling, to eliminate noise from the large and noisy initial training set, resulting in a significant increase of precision. We further improve on this approach by introducing the Semantic Label Propagation method, which uses the similarity between low-dimensional representations of candidate training instances, to extend the training set in order to increase recall while maintaining high precision. Our proposed strategy for generating training data is studied and evaluated on an established test collection designed for knowledge base population tasks. The experimental results show that the Semantic Label Propagation strategy leads to substantial performance gains when compared to existing approaches, while requiring an almost negligible manual annotation effort.
[A]: Knowledge Base Population using Semantic Label Propagation


[Q]: We develop a new semantics for defeasible infer­ ence based on extended probability measures al­ lowed to take infinitesimal values, on the inter­ pretation of defaults as generalized conditional probability constraints and on a preferred-model implementation of entropy-maximization.
[A]: Defaults and lnfinitesimals Defeasible Inference by Nonarchimedean Entropy Maximization


[Q]: <lb>We consider online learning when the time horizon is unknown. We apply a<lb>minimax analysis, beginning with the fixed horizon case, and then moving on to<lb>two unknown-horizon settings, one that assumes the horizon is chosen randomly<lb>according to some known distribution, and the other which allows the adversary<lb>full control over the horizon. For the random horizon setting with restricted losses,<lb>we derive a fully optimal minimax algorithm. And for the adversarial horizon set-<lb>ting, we prove a nontrivial lower bound which shows that the adversary obtains<lb>strictly more power than when the horizon is fixed and known. Based on the mini-<lb>max solution of the random horizon setting, we then propose a new adaptive algo-<lb>rithm which “pretends” that the horizon is drawn from a distribution from a special<lb>family, but no matter how the actual horizon is chosen, the worst-case regret is of<lb>the optimal rate. Furthermore, our algorithm can be generalized in many ways,<lb>including handling other unknown information and other online learning settings.<lb>Experiments show that our algorithm outperforms many other existing algorithms<lb>in an online linear optimization setting.
[A]:
Online Learning with Unknown Time Horizon