Semantic communication, as a revolutionary communication architecture, is
considered a promising novel communication paradigm. Unlike traditional
symbol-based error-free communication systems, semantic-based visual
communication systems extract, compress, transmit, and reconstruct images at
the semantic level. However, widely used image similarity evaluation metrics,
whether pixel-based MSE or PSNR or structure-based MS-SSIM, struggle to
accurately measure the loss of semantic-level information of the source during
system transmission. This presents challenges in evaluating the performance of
visual semantic communication systems, especially when comparing them with
traditional communication systems. To address this, we propose a semantic
evaluation metric -- SeSS (Semantic Similarity Score), based on Scene Graph
Generation and graph matching, which shifts the similarity scores between
images into semantic-level graph matching scores. Meanwhile, semantic
similarity scores for tens of thousands of image pairs are manually annotated
to fine-tune the hyperparameters in the graph matching algorithm, aligning the
metric more closely with human semantic perception. The performance of the SeSS
is tested on different datasets, including (1)images transmitted by traditional
and semantic communication systems at different compression rates, (2)images
transmitted by traditional and semantic communication systems at different
signal-to-noise ratios, (3)images generated by large-scale model with different
noise levels introduced, and (4)cases of images subjected to certain special
transformations. The experiments demonstrate the effectiveness of SeSS,
indicating that the metric can measure the semantic-level differences in
semantic-level information of images and can be used for evaluation in visual
semantic communication systems.

基于场景图生成和图匹配的语义评估指标 SeSS 可以测量图像语义级信息的差异，可用于视觉语义通信系统的评估。

在语义层面衡量视觉相似性的语义相似度分数

Semantic Similarity Score for Measuring Visual Similarity at Semantic  Level

Named entity recognition (NER), which focuses on the extraction of
semantically meaningful named entities and their semantic classes from text,
serves as an indispensable component for several down-stream natural language
processing (NLP) tasks such as relation extraction and event extraction.
Dependency trees, on the other hand, also convey crucial semantic-level
information. It has been shown previously that such information can be used to
improve the performance of NER (Sasano and Kurohashi 2008, Ling and Weld 2012).
In this work, we investigate on how to better utilize the structured
information conveyed by dependency trees to improve the performance of NER.
Specifically, unlike existing approaches which only exploit dependency
information for designing local features, we show that certain global
structured information of the dependency trees can be exploited when building
NER models where such information can provide guided learning and inference.
Through extensive experiments, we show that our proposed novel
dependency-guided NER model performs competitively with models based on
conventional semi-Markov conditional random fields, while requiring
significantly less running time.

该论文探讨了如何更好地利用依存树传达的结构化信息来提高实体识别的性能，新提出的基于依存树指导的实体识别模型在相对较短的运行时间内表现出与传统半 - Markov 条件随机场模型同等的竞争力。