TASK DEFINITION: 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.
PROBLEM: The increasing diversity of languages used on the web introduces a new level of complexity to Information Retrieval (IR) systems. We can no longer assume that textual content is written in one language or even the same language family. In this paper, we demonstrate how to build massive multilingual annotators with minimal human expertise and intervention. We describe a system that builds Named Entity Recognition (NER) annotators for 40 major languages using Wikipedia and Freebase. Our approach does not require NER human annotated datasets or language specific resources like treebanks, parallel corpora, and orthographic rules. The novelty of approach lies therein using only language agnostic techniques, while achieving competitive performance. Our method learns distributed word representations (word embeddings) which encode semantic and syntactic features of words in each language. Then, we automatically generate datasets from Wikipedia link structure and Freebase attributes. Finally, we apply two preprocessing stages (oversampling and exact surface form matching) which do not require any linguistic expertise. Our evaluation is two fold: First, we demonstrate the system performance on human annotated datasets. Second, for languages where no gold-standard benchmarks are available, we propose a new method, distant evaluation, based on statistical machine translation.

SOLUTION: POLYGLOT-NER: Massive Multilingual Named Entity Recognition

PROBLEM: In this paper, we model the trajectory of sea vessels and provide a service that predicts in near-real time the position of any given vessel in 4’, 10’, 20’ and 40’ time intervals. We explore the necessary tradeoffs between accuracy, performance and resource utilization are explored given the large volume and update rates of input data. We start with building models based on well-established machine learning algorithms using static datasets and multi-scan training approaches and identify the best candidate to be used in implementing a single-pass predictive approach, under real-time constraints. The results are measured in terms of accuracy and performance and are compared against the baseline kinematic equations. Results show that it is possible to efficiently model the trajectory of multiple vessels using a single model, which is trained and evaluated using an adequately large, static dataset, thus achieving a significant gain in terms of resource usage while not compromising accuracy.

SOLUTION: Employing traditional machine learning algorithms for big data streams analysis: the case of object trajectory prediction

PROBLEM: Bayesian networks offer great potential for use in automating large scale diagnostic rea­ soning tasks. Gibbs sampling is the main technique used to perform diagnostic reason­ ing in large richly interconnected Bayesian networks. Unfortunately Gibbs sampling can take an excessive time to generate a represen­ tative sample. In this paper we describe and test a number of heuristic strategies for im­ proving sampling in noisy-or Bayesian net­ works. The strategies include Monte Carlo Markov chain sampling techniques other than Gibbs sampling. Emphasis is put on strate­ gies that can be implemented in distributed systems.

SOLUTION:
Improved Sampling for Diagnostic Reasoning in Bayesian Networks