Live languages continuously evolve to integrate the cultural change of human
societies. This evolution manifests through neologisms (new words) or
\textbf{semantic changes} of words (new meaning to existing words).
Understanding the meaning of words is vital for interpreting texts coming from
different cultures (regionalism or slang), domains (e.g., technical terms), or
periods. In computer science, these words are relevant to computational
linguistics algorithms such as translation, information retrieval, question
answering, etc. Semantic changes can potentially impact the quality of the
outcomes of these algorithms. Therefore, it is important to understand and
characterize these changes formally. The study of this impact is a recent
problem that has attracted the attention of the computational linguistics
community. Several approaches propose methods to detect semantic changes with
good precision, but more effort is needed to characterize how the meaning of
words changes and to reason about how to reduce the impact of semantic change.
This survey provides an understandable overview of existing approaches to the
\textit{characterization of semantic changes} and also formally defines three
classes of characterizations: if the meaning of a word becomes more general or
narrow (change in dimension) if the word is used in a more pejorative or
positive/ameliorated sense (change in orientation), and if there is a trend to
use the word in a, for instance, metaphoric or metonymic context (change in
relation). We summarized the main aspects of the selected publications in a
table and discussed the needs and trends in the research activities on semantic
change characterization.

现代计算语言学的研究活动趋势包括语义变化的特征化、新词汇与计算语言学的关系，以及计算语言学算法（如翻译、信息检索、问答等）中语义变化的影响。

语义变化特征研究综述

Survey in Characterization of Semantic Change

The cognitive manifold of published content is currently expanding in all
areas of science. However, Scientific Knowledge Graphs (SKGs) only provide poor
pictures of the adversarial directions and scientific controversies that feed
the production of knowledge. In this Article, we tackle the understanding of
the design of the information space of a cognitive representation of research
activities, and of related bottlenecks that affect search interfaces, in the
mapping of structured objects into graphs. We propose, with SKG GRAPHYP, a
novel graph designed geometric architecture which optimizes both the detection
of the knowledge manifold of "cognitive communities", and the representation of
alternative paths to adversarial answers to a research question, for instance
in the context of academic disputes. With a methodology for designing "Manifold
Subnetworks of Cognitive Communities", GRAPHYP provides a classification of
distinct search paths in a research field. Users are detected from the variety
of their search practices and classified in "Cognitive communities" from the
analysis of the search history of their logs of scientific documentation. The
manifold of practices is expressed from metrics of differentiated uses by
triplets of nodes shaped into symmetrical graph subnetworks, with the following
three parameters: Mass, Intensity, and Variety.

该研究采用一种新的图形化架构，即 SKG GRAPHYP，对认知社区的知识多样性和学术争议中的替代路径进行了优化，提供了研究领域不同搜索路径的分类，可以从其科学文献的搜索历史记录中识别用户并将其分类为 “认知社区” 中的一员。

GRAPHYP: 具有多重子网络社群的科学知识图谱。在对抗信息路线中检测学术争议

GRAPHYP: A Scientific Knowledge Graph with Manifold Subnetworks of Communities. Detection of Scholarly Disputes in Adversarial Information Routes

For extracting meaningful topics from texts, their structures should be
considered properly. In this paper, we aim to analyze structured time-series
documents such as a collection of news articles and a series of scientific
papers, wherein topics evolve along time depending on multiple topics in the
past and are also related to each other at each time. To this end, we propose a
dynamic and static topic model, which simultaneously considers the dynamic
structures of the temporal topic evolution and the static structures of the
topic hierarchy at each time. We show the results of experiments on collections
of scientific papers, in which the proposed method outperformed conventional
models. Moreover, we show an example of extracted topic structures, which we
found helpful for analyzing research activities.

本文提出一个动态和静态主题模型，能同时考虑时间上的主题演化和每个时间的主题层级结构，以此来分析结构化的时间序列文档。作者通过实验表明，该方法在科学论文集的主题提取方面优于传统模型，并展示了提取出的主题结构，以此来帮助对研究活动进行分析。