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]: Chinese word segmentation is a fundamental task for Chinese language processing. The granularity mismatch problem is the main cause of the errors. This paper showed that the binary tree representation can store outputs with different granularity. A binary tree based framework is also designed to overcome the granularity mismatch problem. There are two steps in this framework, namely tree building and tree pruning. The tree pruning step is specially designed to focus on the granularity problem. Previous work for Chinese word segmentation such as the sequence tagging can be easily employed in this framework. This framework can also provide quantitative error analysis methods. The experiments showed that after using a more sophisticated tree pruning function for a state-of-the-art conditional random field based baseline, the error reduction can be up to 20%.
[A]: Binary Tree based Chinese Word Segmentation


[Q]: Dependency parsers are among the most crucial tools in natural language processing as they have many important applications in downstream tasks such as information retrieval, machine translation and knowledge acquisition. We introduce the Yara Parser, a fast and accurate open-source dependency parser based on the arc-eager algorithm and beam search. It achieves an unlabeled accuracy of 93.32 on the standard WSJ test set which ranks it among the top dependency parsers. At its fastest, Yara can parse about 4000 sentences per second when in greedy mode (1 beam). When optimizing for accuracy (using 64 beams and Brown cluster features), Yara can parse 45 sentences per second. The parser can be trained on any syntactic dependency treebank and different options are provided in order to make it more flexible and tunable for specific tasks. It is released with the Apache version 2.0 license and can be used for both commercial and academic purposes. The parser can be found at https: //github.com/yahoo/YaraParser.
[A]: Yara Parser: A Fast and Accurate Dependency Parser


[Q]: Understanding the ways in which participants in public discussions frame their arguments is important in understanding how public opinion is formed. In this paper, we adopt the position that it is time for more computationallyoriented research on problems involving framing. In the interests of furthering that goal, we propose the following specific, interesting and, we believe, relatively accessible question: In the controversy regarding the use of genetically-modified organisms (GMOs) in agriculture, do proand anti-GMO articles differ in whether they choose to adopt a more “scientific” tone? Prior work on the rhetoric and sociology of science suggests that hedging may distinguish popular-science text from text written by professional scientists for their colleagues. We propose a detailed approach to studying whether hedge detection can be used to understanding scientific framing in the GMO debates, and provide corpora to facilitate this study. Some of our preliminary analyses suggest that hedges occur less frequently in scientific discourse than in popular text, a finding that contradicts prior assertions in the literature. We hope that our initial work and data will encourage others to pursue this promising line of inquiry. Publication venue: ACL Workshop on ExtraPropositional Aspects of Meaning in Computational Linguistics, 2012
[A]:
Hedge detection as a lens on framing in the GMO debates: A position paper