Extreme multi-label text classification (XMTC) is an important problem in the
era of big data, for tagging a given text with the most relevant multiple
labels from an extremely large-scale label set. XMTC can be found in many
applications, such as item categorization, web page tagging, and news
annotation. Traditionally most methods used bag-of-words (BOW) as inputs,
ignoring word context as well as deep semantic information. Recent attempts to
overcome the problems of BOW by deep learning still suffer from 1) failing to
capture the important subtext for each label and 2) lack of scalability against
the huge number of labels. We propose a new label tree-based deep learning
model for XMTC, called AttentionXML, with two unique features: 1) a multi-label
attention mechanism with raw text as input, which allows to capture the most
relevant part of text to each label; and 2) a shallow and wide probabilistic
label tree (PLT), which allows to handle millions of labels, especially for
"tail labels". We empirically compared the performance of AttentionXML with
those of eight state-of-the-art methods over six benchmark datasets, including
Amazon-3M with around 3 million labels. AttentionXML outperformed all competing
methods under all experimental settings. Experimental results also show that
AttentionXML achieved the best performance against tail labels among label
tree-based methods. The code and datasets are available at
this http URL .

本研究介绍了一种新的基于标签树的深度学习模型 AttentionXML，它采用多标签注意机制和浅而宽的概率标签树来处理极端多标签文本分类问题，能够对数百万个标签进行处理，并成功地解决了 BOW 和传统深度学习模型在文本分类中的局限性。在几个基准数据集上，AttentionXML 表现出比其他八种最先进的方法更好的性能，期望成为文本分类领域内的研究热点。

AttentionXML：基于标签树的注意力深度模型，用于高性能极端多标签文本分类

AttentionXML: Label Tree-based Attention-Aware Deep Model for  High-Performance Extreme Multi-Label Text Classification

We describe in this report our audio scene recognition system submitted to
the DCASE 2016 challenge. Firstly, given the label set of the scenes, a label
tree is automatically constructed. This category taxonomy is then used in the
feature extraction step in which an audio scene instance is represented by a
label tree embedding image. Different convolutional neural networks, which are
tailored for the task at hand, are finally learned on top of the image features
for scene recognition. Our system reaches an overall recognition accuracy of
81.2% and 83.3% and outperforms the DCASE 2016 baseline with absolute
improvements of 8.7% and 6.1% on the development and test data, respectively.

该论文报道了我们在 DCASE 2016 挑战赛中提交的音频场景识别系统，使用自动构建的标签树进行特征提取，并在其基础上利用卷积神经网络对场景进行识别，取得了相对于基线系统的绝对提升，其开发数据和测试数据的综合识别准确率分别为 81.2% 和 83.3%。