Word meaning changes over time, depending on linguistic and extra-linguistic
factors. Associating a word's correct meaning in its historical context is a
central challenge in diachronic research, and is relevant to a range of NLP
tasks, including information retrieval and semantic search in historical texts.
Bayesian models for semantic change have emerged as a powerful tool to address
this challenge, providing explicit and interpretable representations of
semantic change phenomena. However, while corpora typically come with rich
metadata, existing models are limited by their inability to exploit contextual
information (such as text genre) beyond the document time-stamp. This is
particularly critical in the case of ancient languages, where lack of data and
long diachronic span make it harder to draw a clear distinction between
polysemy (the fact that a word has several senses) and semantic change (the
process of acquiring, losing, or changing senses), and current systems perform
poorly on these languages. We develop GASC, a dynamic semantic change model
that leverages categorical metadata about the texts' genre to boost inference
and uncover the evolution of meanings in Ancient Greek corpora. In a new
evaluation framework, our model achieves improved predictive performance
compared to the state of the art.

针对古代语言语境中词义变化的 challenges，开发了一种新的 GASC 语义变化模型，可以利用文本流派等类别信息提高推理效果，与现有模型相比，GASC 可以更好地预测古希腊语中的词义演变情况。

GASC: 古希腊文体感知语义变化

GASC: Genre-Aware Semantic Change for Ancient Greek

Learning word embeddings has received a significant amount of attention
recently. Often, word embeddings are learned in an unsupervised manner from a
large collection of text. The genre of the text typically plays an important
role in the effectiveness of the resulting embeddings. How to effectively train
word embedding models using data from different domains remains a problem that
is underexplored. In this paper, we present a simple yet effective method for
learning word embeddings based on text from different domains. We demonstrate
the effectiveness of our approach through extensive experiments on various
down-stream NLP tasks.

本文提出了一种基于不同领域文本的词嵌入学习方法，并通过多个下游 NLP 任务的实验，证明了该方法的有效性。