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: In this paper we present the greedy step averaging(GSA) method, a parameter-free stochastic optimization algorithm for a variety of machine learning problems. As a gradient-based optimization method, GSA makes use of the information from the minimizer of a single sample’s loss function, and takes average strategy to calculate reasonable learning rate sequence. While most existing gradient-based algorithms introduce an increasing number of hyper parameters or try to make a trade-off between computational cost and convergence rate, GSA avoids the manual tuning of learning rate and brings in no more hyper parameters or extra cost. We perform exhaustive numerical experiments for logistic and softmax regression to compare our method with the other state of the art ones on 16 datasets. Results show that GSA is robust on various scenarios.

Output: Greedy Step Averaging: A parameter-free stochastic optimization method


Input: Consider Input: In this paper, we introduce a notion of backdoors to Reiter’s propositional default logic and study structural properties of it. Also we consider the problems of backdoor detection (parameterised by the solution size) as well as backdoor evaluation (parameterised by the size of the given backdoor), for various kinds of target classes (cnf, horn, krom, monotone, positive-unit). We show that backdoor detection is fixed-parameter tractable for the considered target classes, and backdoor evaluation is either fixed-parameter tractable, in para-∆P2 , or in para-NP, depending on the target class.

Output: Strong Backdoors for Default Logic


Input: Consider Input: This paper presents the experiments carried out by us at Jadavpur University as part of the participation in FIRE 2015 task: Entity Extraction from Social Media Text Indian Languages (ESM-IL). The tool that we have developed for the task is based on Trigram Hidden Markov Model that utilizes information like gazetteer list, POS tag and some other word level features to enhance the observation probabilities of the known tokens as well as unknown tokens. We submitted runs for English only. A statistical HMM (Hidden Markov Models) based model has been used to implement our system. The system has been trained and tested on the datasets released for FIRE 2015 task: Entity Extraction from Social Media Text Indian Languages (ESM-IL). Our system is the best performer for English language and it obtains precision, recall and F-measures of 61.96, 39.46 and 48.21 respectively.


Output: A Hidden Markov Model Based System for Entity Extraction from Social Media English Text at FIRE 2015