Microsyntactic Unit Detection Using Word Embedding Models: Experiments on S lavic Languages (2023)

Abstract: Microsyntactic units have been defined as language-specific transitional entities between lexicon and grammar, whose idiomatic properties are closely tied to syntax. These units are typically described based on individual constructions, making it difficult to understand them comprehensively as a class. This study proposes a novel approach to detect microsyntactic units using Word Embedding Models (WEMs) trained on six Slavic languages, namely Belarusian, Bulgarian, Czech, Polish, Russian, and Ukrainian, and evaluates how well these models capture the nuances of syntactic non-compositionality. To evaluate the models, we develop a cross-lingual inventory of microsyntactic units using the lists of microsyntantic units available at the Russian National Corpus. Our results demonstrate the effectiveness of WEMs in capturing microsyntactic units across all six Slavic languages under analysis. Additionally, we find that WEMs tailored for syntax-based tasks consistently outperform other WEMs at the task. Our findings contribute to the theory of microsyntax by providing insights into the detection of microsyntactic units and their cross-linguistic properties.

## introduction microsyntactic units, which include syntactic idioms and non-standard syntactic constructions, have been defined as language-specific transitional entities between the lexicon and the grammar, idiomatic properties of which are closely tied to syntax @xcite . these units include all the syntactic units that have very specific and even syntactic properties and do not fit into the standard syntax @xcite . recent research efforts have resulted in the development of several linguistic resources for microsyntactic analysis, such as a microsyntactic dictionary of russian, a microsyntactically annotated corpus of russian texts, and a typology of relevant phenomena @xcite @xcite . given the vast number and diverse nature of microsyntactic phenomena, it is not surprising that they are often described on the basis of individual constructions or small classes of syntactic phrases. in order to gain a more comprehensive and systematic understanding of these phenomena, it is crucial to attempt an analysis of microsyntactic phenomena at scale, rather than in isolation. in this study, we add to the line of research on microsyntax by adapting quantitative and computational methods used in idiom recognition for identification of microsyntactic units in large corpora of texts and across different languages. we apply different types of word embedding models (wems) to the task of microsyntactic unit detection, and test their performance on five functional categories of microsyntactic unit (prepositions, adverbials and predicatives, parenthetical expressions, conjunctions, and particles) in six slavic languages @xcite . concretely, the contributions of this paper are as follows: our study not only contributes to the theory of microsyntax but also has practical applications in natural language processing, machine translation, and other areas of computational linguistics where effective handling of non-standard syntactic structures is required. after presenting the relevant background in section 2, we introduce the used methods, data and models in section 3. the obtained results are discussed in section 4, and finally, the conclusions are drawn in section 5. the code used for our experiments is available at github.com/iuliiazaitova/microsyntactic-unit-detection-using-word-embedding-models-slavic-languages. ## results and discussion the experimental findings for the five models trained on 100,000 and 500,000 sentences are summarized in tables 2 and 3 , respectively. on the datasets of 100,000 sentences ( as for word2vec structured skip-gram, we know that similarly to the word2vec skip-gram architecture (section 3.5), it predicts the context tokens given the center token. in the case of syntactic compositionality prediction, where the relationships between words within a phrase are crucial, it could be more advantageous to predict the center token and capture information from its sentential context, which helps in understanding the sentence's structure. to better interpret why word2vec structured skip-gram, despite its generally low performance, significantly outperforms other models in the case of the russian language (500,000 sentences), it is helpful to compare the results by category of microsyntactic units. figures 1 and 2 depict violin plots of cosine similarity scores by category for the two best performing models trained on the 500,000 sentence dataset in russian. a clear difference is observable between the distributions of microsyntactic units and compositional units on both plots. moreover, we can see that the distribution of cosine similarity scores for microsyntactic units is wider for the structured skip-gram model, while there is an opposite tendency in the cwindow plots, where compositional units seem to have a wider range of scores. the wider distribution of cosine similarity scores, which influences the quality of ordering, could be one of the factors that contributed to the observed outlier in the map score. from the violin plots, we can also see that some unit types show a higher difference from compositional units. one explanation for that is that some types, such as adverbial and predicative constructions, additionally possess a lower degree of semantic non-compositionality, to which our models are sensitive. figure 3 represents the average map scores for models trained on 500,000 sentences, grouped by category and averaged across languages. this figure further supports the observation of varying model performance across different linguistic categories. certain categories (adverbial and predicative, particles) consistently exhibit higher scores across all models, indicating that their noncompositionality is easier for the models to predict. similarly, prepositions consistently yield lower scores. ## conclusion and future work in this paper, we presented a novel approach for using wems for microsyntactic unit recognition in six slavic languages. we have built a multilingual comparative database of microsyntactic units in six slavic languages, each with six sets of parallel bilingual context sentences. our comparative evaluation of word2vec cbow, word2vec cwindow, word2vec structured skip-gram, context2vec and node2vec models suggests that wems can be effective for non-compositionality prediction, and that wems adapted to syntax-based tasks outperform other types of wems. the analysis of results shows that there are some differences in the performance of microsyntactic unit recognition across types of these units. in this vein, we have observed that different languages tend to produce similar results across different types of microsyntactic units. in our future work, we are interested in improving the results for microsyntactic unit recognition. this includes investigating the use of additional features or data sources to improve model performance, as well as exploring different modeling architectures, such as large language models. additionally, the inconsistent results of microsyntactic unit recognition when split by category also highlight the importance of evaluating models on different types of syntactic non-compositionality. finally, we plan to explore the use of our database in practical applications, such as improving machine translation systems and using our models as