Animacy detection is a problem whose solution has been shown to be beneficial for a number of syntactic and semantic tasks.We present a state-of-the-art system for this task which uses a number of simple classifiers with heterogeneous data sources in a voting scheme.We show how this framework can give us direct insight into the behavior of the system, allowing us to more easily diagnose sources of error.

We present MCTest, a freely available set of stories and associated questions intended for research on the machine comprehension of text.Previous work on machine comprehension (e.g., semantic modeling) has made great strides, but primarily focuses either on limited-domain datasets, or on solving a more restricted goal (e.g., open-domain relation extraction).In contrast, MCTest requires machines to answer multiple-choice reading comprehension questions about fictional stories, directly tackling the high-level goal of open-domain machine comprehension.Reading comprehension can test advanced abilities such as causal reasoning and understanding the world, yet, by being multiple-choice, still provide a clear metric.By being fictional, the answer typically can be found only in the story itself.The stories and questions are also carefully limited to those a young child would understand, reducing the world knowledge that is required for the task.We present the scalable crowd-sourcing methods that allow us to cheaply construct a dataset of 500 stories and 2000 questions.By screening workers (with grammar tests) and stories (with grading), we have ensured that the data is the same quality as another set that we manually edited, but at one tenth the editing cost.By being open-domain, yet carefully restricted, we hope MCTest will serve to encourage research and provide a clear metric for advancement on the machine comprehension of text.

Audio fingerprinting is a powerful tool for identifying file-based or streaming audio, using a database of fingerprints. The paper presents two new applications of audio fingerprinting: duplicate detection, whose goal is to identify duplicate audio clips in a set, even if they differ in compression quality or duration, and thumbnail generation, which aims to provide a representative short clip of a music track. Neither application requires an external database of fingerprints. Thanks to the robustness of the fingerprinting engine, both applications perform well; the duplicate detector has a false positive rate that is conservatively bounded above by 1% on a very large data set, and the thumbnail generator significantly outperforms using a fixed window.

We investigate using gradient descent methods for learning ranking functions; we propose a simple probabilistic cost function, and we introduce RankNet, an implementation of these ideas using a neural network to model the underlying ranking function. We present test results on toy data and on data from a commercial internet search engine. 1.

Many bitmap documents are composed by the superposition of layers with pictures and text. These documents do not compress well using image compression algorithms such as JPEG-2000, because text introduces sharp edges on top of the smooth surfaces typically found in natural images. Similarly, compression algorithms for text facsimiles, such as JBIG2, are not suited for color or gray level images. In this paper the SLIm system for separating text and line drawing from background images, in order to compress both more effectively is proposed. This approach differ from previous ones such as DjVu, Tiff-FX, and MRC, by being extremely simple and fast, while yielding close to state-of-the-art compression performance. Results show that the SLIm compression performance is attractive for many applications.