Open information extraction (OIE) aims to extract surface relations and their
corresponding arguments from natural language text, irrespective of domain.
This paper presents an innovative OIE model, APRCOIE, tailored for Chinese
text. Diverging from previous models, our model generates extraction patterns
autonomously. The model defines a new pattern form for Chinese OIE and proposes
an automated pattern generation methodology. In that way, the model can handle
a wide array of complex and diverse Chinese grammatical phenomena. We design a
preliminary filter based on tensor computing to conduct the extraction
procedure efficiently. To train the model, we manually annotated a large-scale
Chinese OIE dataset. In the comparative evaluation, we demonstrate that APRCOIE
outperforms state-of-the-art Chinese OIE models and significantly expands the
boundaries of achievable OIE performance. The code of APRCOIE and the annotated
dataset are released on GitHub (this https URL)

本研究提出了一种创新的 APRCOIE 模型，专门用于处理中文文本，通过自动生成抽取模式，可以处理各种复杂多样的中文语法现象，与现有的中文 OIE 模型相比，APRCOIE 表现更好并显著扩展了 OIE 性能的界限。

开放信息提取仍然适用规则

Rules still work for Open Information Extraction

Text normalization, defined as a procedure transforming non standard words to
spoken-form words, is crucial to the intelligibility of synthesized speech in
text-to-speech system. Rule-based methods without considering context can not
eliminate ambiguation, whereas sequence-to-sequence neural network based
methods suffer from the unexpected and uninterpretable errors problem. Recently
proposed hybrid system treats rule-based model and neural model as two cascaded
sub-modules, where limited interaction capability makes neural network model
cannot fully utilize expert knowledge contained in the rules. Inspired by
Flat-LAttice Transformer (FLAT), we propose an end-to-end Chinese text
normalization model, which accepts Chinese characters as direct input and
integrates expert knowledge contained in rules into the neural network, both
contribute to the superior performance of proposed model for the text
normalization task. We also release a first publicly accessible largescale
dataset for Chinese text normalization. Our proposed model has achieved
excellent results on this dataset.

提出了一种中文文本归一化模型，将规则中的专家知识融合到神经网络中，从而提高了性能。同时，公开了一份中文文本归一化的大规模数据集。

基于规则引导的平面格变换器的端到端中文文本规范化模型

An End-to-end Chinese Text Normalization Model based on Rule-guided Flat-Lattice Transformer

The task of Chinese text spam detection is very challenging due to both glyph
and phonetic variations of Chinese characters. This paper proposes a novel
framework to jointly model Chinese variational, semantic, and contextualized
representations for Chinese text spam detection task. In particular, a
Variation Family-enhanced Graph Embedding (VFGE) algorithm is designed based on
a Chinese character variation graph. The VFGE can learn both the graph
embeddings of the Chinese characters (local) and the latent variation families
(global). Furthermore, an enhanced bidirectional language model, with a
combination gate function and an aggregation learning function, is proposed to
integrate the graph and text information while capturing the sequential
information. Extensive experiments have been conducted on both SMS and review
datasets, to show the proposed method outperforms a series of state-of-the-art
models for Chinese spam detection.

本文提出了一种基于中文字符变体图的变体家族增强图嵌入算法（VFGE），以及一种结合了图和文本信息的增强双向语言模型，用于中文文本垃圾检测任务，实验结果表明该方法在短信和评论数据集上表现优于一系列最先进的模型。

基于 Graph 和 Text 联合嵌入的中国汉字变体表示，用于探测伪装垃圾内容

Detect Camouflaged Spam Content via StoneSkipping: Graph and Text Joint  Embedding for Chinese Character Variation Representation

We introduce Chinese Text in the Wild, a very large dataset of Chinese text
in street view images. While optical character recognition (OCR) in document
images is well studied and many commercial tools are available, detection and
recognition of text in natural images is still a challenging problem,
especially for more complicated character sets such as Chinese text. Lack of
training data has always been a problem, especially for deep learning methods
which require massive training data.
In this paper we provide details of a newly created dataset of Chinese text
with about 1 million Chinese characters annotated by experts in over 30
thousand street view images. This is a challenging dataset with good diversity.
It contains planar text, raised text, text in cities, text in rural areas, text
under poor illumination, distant text, partially occluded text, etc. For each
character in the dataset, the annotation includes its underlying character, its
bounding box, and 6 attributes. The attributes indicate whether it has complex
background, whether it is raised, whether it is handwritten or printed, etc.
The large size and diversity of this dataset make it suitable for training
robust neural networks for various tasks, particularly detection and
recognition. We give baseline results using several state-of-the-art networks,
including AlexNet, OverFeat, Google Inception and ResNet for character
recognition, and YOLOv2 for character detection in images. Overall Google
Inception has the best performance on recognition with 80.5% top-1 accuracy,
while YOLOv2 achieves an mAP of 71.0% on detection. Dataset, source code and
trained models will all be publicly available on the website.

介绍一个街景图中的大规模中文文本数据集，该数据集包含超过 30,000 张街景图中的大约 1 百万个中文字符，同时提供基准结果使用 AlexNet，OverFeat，Google Inception 和 ResNet 进行字符识别，使用 YOLOv2 进行字符检测。