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.
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Question: 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.

Answer: POLYGLOT-NER: Massive Multilingual Named Entity Recognition


Question: More and more of the information on the web is dialogic, from Facebook newsfeeds, to forum conversations, to comment threads on news articles. In contrast to traditional, monologic Natural Language Processing resources such as news, highly social dialogue is frequent in social media, making it a challenging context for NLP. This paper tests a bootstrapping method, originally proposed in a monologic domain, to train classifiers to identify two different types of subjective language in dialogue: sarcasm and nastiness. We explore two methods of developing linguistic indicators to be used in a first level classifier aimed at maximizing precision at the expense of recall. The best performing classifier for the first phase achieves 54% precision and 38% recall for sarcastic utterances. We then use general syntactic patterns from previous work to create more general sarcasm indicators, improving precision to 62% and recall to 52%. To further test the generality of the method, we then apply it to bootstrapping a classifier for nastiness dialogic acts. Our first phase, using crowdsourced nasty indicators, achieves 58% precision and 49% recall, which increases to 75% precision and 62% recall when we bootstrap over the first level with generalized syntactic patterns.

Answer: Really? Well. Apparently Bootstrapping Improves the Performance of Sarcasm and Nastiness Classifiers for Online Dialogue


Question: Minimizing the rank of a matrix subject to affine constraints is a fundamental problem with many important applications in machine learning and statistics. In this paper we propose a simple and fast algorithm SVP (Singular Value Projection) for rank minimization with affine constraints (ARMP) and show that SVP recovers the minimum rank solution for affine constraints that satisfy the restricted isometry property. We show robustness of our method to noise with a strong geometric convergence rate even for noisy measurements. Our results improve upon a recent breakthrough by Recht, Fazel and Parillo [RFP07] in three significant ways: 1) our method (SVP) is significantly simpler to analyse and easier to implement, 2) we give geometric convergence guarantees for SVP and, as demonstrated empiricially, SVP is significantly faster on real-world and synthetic problems, 3) we give optimality and geometric convergence guarantees even for the noisy version of ARMP. In addition, we address the practically important problem of low-rank matrix completion, which can be seen as a special case of ARMP. However, the affine constraints defining the matrix-completion problem do not obey the restricted isometry property in general. We empirically demonstrate that our algorithm recovers low-rank incoherent matrices from an almost optimal number of uniformly sampled entries. We make partial progress towards proving exact recovery and provide some intuition for the performance of SVP applied to matrix completion by showing a more restricted isometry property. Our algorithm outperforms existing methods, such as those of [RFP07, CR08, CT09, CCS08, KOM09], for ARMP and the matrix-completion problem by an order of magnitude and is also significantly more robust to noise.

Answer:
Guaranteed Rank Minimization via Singular Value Projection