Abstract

The paper analyses the correlation of change in word concreteness ratings with semantic change. To perform the analysis, we apply a neural network to diachronic data to obtain concreteness ratings of English words. As input to the model, we use co-occurrence statistics with the most frequent words extracted from the Google Books Ngram diachronic corpus. It is shown that the model, initially trained on data averaged over a long time interval, predicts the concreteness ratings with high accuracy (based on the word co-occurrence data in a particular year). The impact of lexical semantic change on the change in the concreteness rating is analyzed using 69 words borrowed from previous works. As the considered cases show, the neural network estimate of the word concreteness rating is very sensitive to changes in semantics. Among the factors that influence changes in the concreteness rating, we reveal the emergence of new meanings of a word, the competition of word meanings related to different parts of speech, the use of a word as a proper name, and the use of the word as a part of collocations. It is shown in the paper that changes in the concreteness rating can (along with changes in other word properties) serve as a marker of semantic change.

中文翻译：

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单词具体性评级的历时分析：语义变化的影响

 抽象的

本文分析了单词具体性评级变化与语义变化的相关性。为了进行分析，我们将神经网络应用于历时数据以获得英语单词的具体性评级。作为模型的输入，我们使用从 Google Books Ngram 历时语料库中提取的最常见单词的共现统计数据。结果表明，该模型最初是根据长时间间隔的平均数据进行训练的，可以高精度地预测具体性评级（基于特定年份的单词共现数据）。借用前人作品中的 69 个单词，分析了词汇语义变化对具体性评分变化的影响。正如所考虑的案例所示，单词具体性评级的神经网络估计对语义的变化非常敏感。在影响具体性评级变化的因素中，我们揭示了单词新含义的出现、与不同词性相关的单词含义的竞争、单词作为专有名称的使用以及单词的使用作为搭配的一部分。论文表明，具体性评级的变化（以及其他单词属性的变化）可以作为语义变化的标志。