This paper explores grammaticization of deverbal prepositions by a computational approach based on corpus data. Deverbal prepositions are words or phrases that are derived from a verb and that behave as a preposition such as “regarding” and “according to”. Linguistic studies have revealed important aspects of grammaticization of deverbal prepositions. This paper augments them by methods for measuring the degree of grammaticization of deverbal prepositions based on non-contextualized or contextualized word vectors. Experiments show that the methods correlate well with human judgements (as high as 0.69 in Spearman’s rank correlation coefficient). Using the best-performing method, this paper further shows that the methods support previous findings in linguistics including (i) Deverbal prepositions are marginal in terms of prepositionality; and (ii) The process where verbs are grammaticized into prepositions is gradual. As a pilot study, it also conducts a diachronic analysis of grammaticization of deverbal preposition.

In this paper, we propose methods 1 for discovering semantic differences in words appearing in two corpora.The key idea is to measure the coverage of meanings of a word in a corpus through the norm of its mean word vector, which is equivalent to examining a kind of variance of the word vector distribution.The proposed methods do not require alignments between words and/or corpora for comparison that previous methods do.All they require are to compute variance (or norms of mean word vectors) for each word type.Nevertheless, they rival the best-performing system in the SemEval-2020 Task 1.In addition, they are (i) robust for the skew in corpus sizes; (ii) capable of detecting semantic differences in infrequent words; and (iii) effective in pinpointing word instances that have a meaning missing in one of the two corpora under comparison.We show these advantages for historical corpora and also for native/non-native English corpora.

This article revisits statistical relationships across Romance cognates between lexical semantic shift and six intra-linguistic variables, such as frequency and polysemy. Cognates are words that are derived from a common etymon, in this case, a Latin ancestor. Despite their shared etymology, some cognate pairs have experienced semantic shift. The degree of semantic shift is quantified using cosine distance between the cognates’ corresponding word embeddings. In the previous literature, frequency and polysemy have been reported to be correlated with semantic shift; however, the understanding of their effects needs revision because of various methodological defects. In the present study, we perform regression analysis under improved experimental conditions, and demonstrate a genuine negative effect of frequency and positive effect of polysemy on semantic shift. Furthermore, we reveal that morphologically complex etyma are more resistant to semantic shift and that the cognates that have been in use over a longer timespan are prone to greater shift in meaning. These findings add to our understanding of the historical process of semantic change.

We analyze semantic changes in loanwords from English that are used in Japanese (Japanese loanwords). Specifically, we create word embeddings of English and Japanese and map the Japanese embeddings into the English space so that we can calculate the similarity of each Japanese word and each English word. We then attempt to find loanwords that are semantically different from their original, see if known meaning changes are correctly captured, and show the possibility of using our methodology in language education.