Information extraction can support novel and effective access paths for
digital libraries. Nevertheless, designing reliable extraction workflows can be
cost-intensive in practice. On the one hand, suitable extraction methods rely
on domain-specific training data. On the other hand, unsupervised and open
extraction methods usually produce not-canonicalized extraction results. This
paper tackles the question how digital libraries can handle such extractions
and if their quality is sufficient in practice. We focus on unsupervised
extraction workflows by analyzing them in case studies in the domains of
encyclopedias (Wikipedia), pharmacy and political sciences. We report on
opportunities and limitations. Finally we discuss best practices for
unsupervised extraction workflows.

本文介绍信息抽取如何支持数字图书馆，讨论数字图书馆如何处理无监督抽取并报告机会和局限性，最后探讨无监督抽取工作流程的最佳实践。

数字图书馆中几乎无监督信息提取工作流的图书馆视角

A Library Perspective on Nearly-Unsupervised Information Extraction Workflows in Digital Libraries

Unsupervised extraction of objects from low-level visual data is an important
goal for further progress in machine learning. Existing approaches for
representing objects without labels use structured generative models with
static images. These methods focus a large amount of their capacity on
reconstructing unimportant background pixels, missing low contrast or small
objects. Conversely, we present a new method that avoids losses in pixel space
and over-reliance on the limited signal a static image provides. Our approach
takes advantage of objects' motion by learning a discriminative,
time-contrastive loss in the space of slot representations, attempting to force
each slot to not only capture entities that move, but capture distinct objects
from the other slots. Moreover, we introduce a new quantitative evaluation
metric to measure how "diverse" a set of slot vectors are, and use it to
evaluate our model on 20 Atari games.

本文介绍了一种无监督抽取物体的方法，在机器学习中具有重要意义。该方法不使用标签，通过利用物体的运动来学习区分每个对象，并使用新的量化评估指标，在 20 个 Atari 游戏中对其性能进行评估。

区别性对比网络：一种表示对象的对比方法

Slot Contrastive Networks: A Contrastive Approach for Representing  Objects

We consider the task of unsupervised extraction of meaningful latent
representations of speech by applying autoencoding neural networks to speech
waveforms. The goal is to learn a representation able to capture high level
semantic content from the signal, e.g.\ phoneme identities, while being
invariant to confounding low level details in the signal such as the underlying
pitch contour or background noise. Since the learned representation is tuned to
contain only phonetic content, we resort to using a high capacity WaveNet
decoder to infer information discarded by the encoder from previous samples.
Moreover, the behavior of autoencoder models depends on the kind of constraint
that is applied to the latent representation. We compare three variants: a
simple dimensionality reduction bottleneck, a Gaussian Variational Autoencoder
(VAE), and a discrete Vector Quantized VAE (VQ-VAE). We analyze the quality of
learned representations in terms of speaker independence, the ability to
predict phonetic content, and the ability to accurately reconstruct individual
spectrogram frames. Moreover, for discrete encodings extracted using the
VQ-VAE, we measure the ease of mapping them to phonemes. We introduce a
regularization scheme that forces the representations to focus on the phonetic
content of the utterance and report performance comparable with the top entries
in the ZeroSpeech 2017 unsupervised acoustic unit discovery task.

本研究旨在通过自编码神经网络从语音波形中提取有意义的潜在表示，并比较三个变体：简单维度约束、高斯变分自编码器和离散量化矢量自编码器，成果表明此方法在 ZeroSpeech 2017 任务中获得了可比拟的表现。