We propose a solution for the LSCDiscovery shared task on Lexical Semantic
Change Detection in Spanish. Our approach is based on generating lexical
substitutes that describe old and new senses of a given word. This approach
achieves the second best result in sense loss and sense gain detection
subtasks. By observing those substitutes that are specific for only one time
period, one can understand which senses were obtained or lost. This allows
providing more detailed information about semantic change to the user and makes
our method interpretable.

该研究提出了一种基于生成旧新义词的词汇替代方案，它在探测语义变化中实现了第二佳的结果，并且通过观察仅出现在一个时间段的替代词可以理解哪些义项被获得或丢失，从而为用户提供了更详细的信息并使方法具有可解释性。

LSCDiscovery 中的 BOS: 用于可解释的词汇语义变化检测的词汇替换

BOS at LSCDiscovery: Lexical Substitution for Interpretable Lexical Semantic Change Detection

We describe a new addition to the WebVectors toolkit which is used to serve
word embedding models over the Web. The new ELMoViz module adds support for
contextualized embedding architectures, in particular for ELMo models. The
provided visualizations follow the metaphor of `two-dimensional text' by
showing lexical substitutes: words which are most semantically similar in
context to the words of the input sentence. The system allows the user to
change the ELMo layers from which token embeddings are inferred. It also
conveys corpus information about the query words and their lexical substitutes
(namely their frequency tiers and parts of speech). The module is well
integrated into the rest of the WebVectors toolkit, providing lexical
hyperlinks to word representations in static embedding models. Two web services
have already implemented the new functionality with pre-trained ELMo models for
Russian, Norwegian and English.

该研究论文介绍了一种名为 WebVectors 的工具包，其中新增了 ELMoViz 模块，将支持上下文嵌入架构，特别是 ELMo 模型，提供的可视化工具为‘二维文本’元喻，展示了语境中语义最相似的词语选项，还支持用户可更改 ELMo 层次，同时传达了查询词及其词汇替代物（即其频率级别和词性）的语料库信息，并且模块已经很好的整合到 WebVectors 工具包中，提供了静态嵌入模型中的词表示的词汇超链接，已有两个 Web 服务实现了俄语、挪威语和英语的预训练 ELMo 模型。

将 ELMo 嵌入表示为二维文本在线

Representing ELMo embeddings as two-dimensional text online

Lexical substitutes have found use in areas such as paraphrasing, text
simplification, machine translation, word sense disambiguation, and part of
speech induction. However the computational complexity of accurately
identifying the most likely substitutes for a word has made large scale
experiments difficult. In this paper I introduce a new search algorithm,
FASTSUBS, that is guaranteed to find the K most likely lexical substitutes for
a given word in a sentence based on an n-gram language model. The computation
is sub-linear in both K and the vocabulary size V. An implementation of the
algorithm and a dataset with the top 100 substitutes of each token in the WSJ
section of the Penn Treebank are available at this http URL

本论文介绍了一种基于 n-gram 语言模型的新搜索算法 FASTSUBS, 用于精确定位给定句子中最有可能的词汇替换，可应用于诸如文本简化、机器翻译和词性归纳等领域，算法计算复杂度在 K 和词汇表大小 V 方面具有亚线性的特点。