To resolve the semantic ambiguity in texts, we propose a model, which
innovatively combines a knowledge graph with an improved attention mechanism.
An existing knowledge base is utilized to enrich the text with relevant
contextual concepts. The model operates at both character and word levels to
deepen its understanding by integrating the concepts. We first adopt
information gain to select import words. Then an encoder-decoder framework is
used to encode the text along with the related concepts. The local attention
mechanism adjusts the weight of each concept, reducing the influence of
irrelevant or noisy concepts during classification. We improve the calculation
formula for attention scores in the local self-attention mechanism, ensuring
that words with different frequencies of occurrence in the text receive higher
attention scores. Finally, the model employs a Bi-directional Gated Recurrent
Unit (Bi-GRU), which is effective in feature extraction from texts for improved
classification accuracy. Its performance is demonstrated on datasets such as
AGNews, Ohsumed, and TagMyNews, achieving accuracy of 75.1%, 58.7%, and 68.5%
respectively, showing its effectiveness in classifying tasks.

为了解决文本中的语义模糊问题，我们提出了一个模型，创新地将知识图谱与改进的注意机制结合起来。该模型在字符和词级别上运作，通过集成概念来加深对文本的理解。我们首先采用信息增益选择重要词，然后采用编码器 - 解码器框架对文本及相关概念进行编码。本地注意机制调整每个概念的权重，在分类过程中减少不相关或噪声概念的影响。我们改进了本地自注意机制中注意分数的计算公式，确保文本中不同频率出现的词语获得更高的注意分数。最后，该模型采用了双向门控循环单元（Bi-GRU），从文本中提取特征以提高分类准确性。该模型在 AGNews、Ohsumed 和 TagMyNews 等数据集上达到了 75.1%、58.7% 和 68.5% 的准确率，展示了其在分类任务中的有效性。

基于知识图谱和改进注意力机制的文本分类

Text Classification Based on Knowledge Graphs and Improved Attention  Mechanism

With a rising attention for the issue of PM2.5 or PM0.3, particulate matters
have become not only a potential threat to both the environment and human, but
also a harming existence to instruments onboard International Space Station
(ISS). Our team is aiming to relate various concentration of particulate
matters to magnetic fields, humidity, acceleration, temperature, pressure and
CO2 concentration. Our goal is to establish an early warning system (EWS),
which is able to forecast the levels of particulate matters and provides ample
reaction time for astronauts to protect their instruments in some experiments
or increase the accuracy of the measurements; In addition, the constructed
model can be further developed into a prototype of a remote-sensing smoke alarm
for applications related to fires. In this article, we will implement the
Bi-GRU (Bidirectional Gated Recurrent Unit) algorithms that collect data for
past 90 minutes and predict the levels of particulates which over 2.5
micrometer per 0.1 liter for the next 1 minute, which is classified as an early
warning

本文旨在通过建立早期预警系统和远程感应烟雾报警器，将 PM2.5/0.3 颗粒物等浓度与磁场、湿度、加速度、温度、压力和 CO2 浓度关联起来，从而为宇航员提供适当的反应时间来保护他们的仪器，并提高测量的准确性。我们实现了 Bi-GRU 算法来预测未来 1 分钟内 PM 超过 2.5 微米 / 每 0.1 升的颗粒物浓度。

国际空间站上基于 Bi-GRU 的自动紧急无尘解决方案（AED-ISS）

Automatic Emergency Dust-Free solution on-board International Space Station with Bi-GRU (AED-ISS)

Relation extraction is the problem of classifying the relationship between
two entities in a given sentence. Distant Supervision (DS) is a popular
technique for developing relation extractors starting with limited supervision.
We note that most of the sentences in the distant supervision relation
extraction setting are very long and may benefit from word attention for better
sentence representation. Our contributions in this paper are threefold.
Firstly, we propose two novel word attention models for distantly- supervised
relation extraction: (1) a Bi-directional Gated Recurrent Unit (Bi-GRU) based
word attention model (BGWA), (2) an entity-centric attention model (EA), and
(3) a combination model which combines multiple complementary models using
weighted voting method for improved relation extraction. Secondly, we introduce
GDS, a new distant supervision dataset for relation extraction. GDS removes
test data noise present in all previous distant- supervision benchmark
datasets, making credible automatic evaluation possible. Thirdly, through
extensive experiments on multiple real-world datasets, we demonstrate the
effectiveness of the proposed methods.

本论文介绍了两种新的用于远程监督下关系抽取的单词注意模型，同时提出了一种组合模型，并在多个真实数据集上进行了广泛的实验来证明这些方法的有效性。