RETRACTED: Testing of statistical significance of semantic changes detected by diachronic word embedding

Bochkarev, Vladimir V.; Maslennikova, Yulia; Shevlyakova, Anna V.

doi:10.3233/jifs-212179

Cited by 4 publications

(3 citation statements)

References 5 publications

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“…In such case, the correctness of the conclusions made in the work depends on the validity of the assumption put forward. Note that there is no theoretical argument in favor of the power-law dependence in the literature for this case, and the results of model experiments also do not confirm it (see [33]).…”

Section: Accounting For the Impact Of Word Frequencymentioning

confidence: 73%

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How Fast Do Distribution and Semantics of Polysemic Words Change?

Bochkarev,

Shevlyakova

2024

J. Phys.: Conf. Ser.

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Creation of large diachronic text corpora triggered studies of evolution of natural languages using quantitative methods in the last decade. For the first time, it became possible to raise questions about general patterns of lexical semantic change. In the work by W. Hamilton et al., 2016, a hypothesis was formulated about a more rapid change in the meanings of polysemic words. In this paper, we consider how frequency of words influence variations of semantic metrics. We use explicit word vector representations and Jensen-Shannon divergence as a measure of change in the distribution and semantics of a word. The obtained results show that changes in the frequency ratio of a word in different meanings can themselves lead to a change in a word distribution, even in the absence of any changes in its meaning. Therefore, the observations made in the work by W. Hamilton et al about correlation between the rate of change of words and their local cluster coefficient in the semantic connection network (considered as a quantitative measure of polysemy) can be fully explained by the effect described in the article.

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Section: Accounting For the Impact Of Word Frequencymentioning

confidence: 73%

“…It is known that for all types of word embeddings, semantic difference estimates strongly depend on word frequency [3,5,32,31,33]. As pointed out in [5], this effect must necessarily be taken into account when comparing the rate of semantic changes for different words.…”

Section: Accounting For the Impact Of Word Frequencymentioning

confidence: 99%

How Fast Do Distribution and Semantics of Polysemic Words Change?

Bochkarev,

Shevlyakova

2024

J. Phys.: Conf. Ser.

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“…In addition to the model parameters and settings in the training process, a significant role in estimating the quality of models can be played by random distribution of weights when initializing the model at the beginning of the training. Work [9] shows that repeated training of word2vec models allows one to estimate confidence intervals for analyzing significance of changes in semantic distances between words.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of word embedding models used for diachronic semantic change analysis

Maslennikova,

Bochkarev

2024

J. Phys.: Conf. Ser.

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In the last decade, the quantitative analysis of diachronic changes in language and lexical semantic changes have become the subject of active research. A significant role was played by the development of new effective techniques of word embedding. This direction has been effectively demonstrated in a number of studies. Some of them have focused on the analysis of the optimal type of word2vec models, hyperparameters for training, and evaluation techniques. In this research, we used Corpus of Historical American English (COHA). The paper demonstrates the results of multiple training runs and the comparison of word2vec models with different variations of hyperparameters used for lexical semantic change detection. In addition to traditional word similarities and analogical reasoning tests, we used testing on an extended set of synonyms. We have evaluated word2vec models on the set of more than 100,000 English synsets that were randomly selected from the WordNet database. We have shown that changing the word2vec model parameters (such as a dimension of word embedding, a size of context window, a type of model, a word discard rate etc.) can significantly impact on the resulting word embedding vector space and the detected lexical semantic changes. Additionally, the results strongly depended on properties of the corpus, such as word frequency distribution.

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