Predicting Compositionality of Verbal Multiword Expressions in P ersian (2023)

Abstract: The identification of Verbal Multiword Expressions (VMWEs) presents a greater challenge compared to non-verbal MWEs due to their higher surface variability. VMWEs are linguistic units that exhibit varying levels of semantic opaqueness and pose difficulties for computational models in terms of both their identification and the degree of compositionality. In this study, a new approach to predicting the compositional nature of VMWEs in Persian is presented. The method begins with an automatic identification of VMWEs in Persian sentences, which is approached as a sequence labeling problem for recognizing the components of VMWEs. The method then creates word embeddings that better capture the semantic properties of VMWEs and uses them to determine the degree of compositionality through multiple criteria. The study compares two neural architectures for identification, BiLSTM and ParsBERT, and shows that a fine-tuned BERT model surpasses the BiLSTM model in evaluation metrics with an F1 score of 89%. Next, a word2vec embedding model is trained to capture the semantics of identified VMWEs and is used to estimate their compositionality, resulting in an accuracy of 70.9% as demonstrated by experiments on a collected dataset of expert-annotated compositional and non-compositional VMWEs.

## introduction in today's world, multiword expression detection and embedding are trending topics, particularly among the research conducted on natural language processing. multiword expressions (mwes) are word combinations that  these two authors contributed equally to this work display some form of idiomaticity, in which the semantics of some of the mwes cannot be predicted from the semantics of their component. these expressions comprised of at least two words, inclusive of a headword and syntactically related words that display some degree of lexical, morphological, syntactic, and/or semantic idiosyncrasy @xcite . in this paper, we focus on verbal mwe (vmwe) which is a multiword expression such that its syntactic head is a verb and its other components are directly dependent on the verb @xcite . identifying a vmwe in a persian sentence poses many challenges, like in other languages @xcite . one of the primary ones is the violation of the compositionality principle, leading to the inability to deduce the semantic meaning of the vmwe from the meanings of its individual components as shown in (1). in section 2, a review of existing methods is presented. the proposed algorithm for identification and prediction of compositionality is detailed in section 3 and 4, respectively. the effectiveness of the introduced approaches is assessed through experiments, the results of which are presented in section 5. finally, in section 6, the results are discussed and concluding remarks are drawn. ## related work vmwes identification: there are generally two types of methods to identify vmwes in a sentence: language-dependent and languageindependent methods. in terms of languagedependent methods, @xcite introduced an unsupervised method to identify persian vmwes by defining a set of linguistic rules. @xcite also introduced a language-dependent method to identify persian mwes by creating regular expressions by persian linguistic rules and searching extracted mwes from wikipedia article titles and farsnet @xcite . moreover, @xcite introduced a method that utilized a bilingual parallel corpus and evaluated the efficacy of seven linguistically-informed features in automatically detecting persian lvcs with the aid of two classifiers. in recent years, deep learning has demonstrated remarkable success in sequence tagging tasks, including mwe identification @xcite . rnns and convnets have shown significant progress in this area. @xcite achieved their best results on the dimsum @xcite dataset using a convnet architecture to identify mwes. @xcite proposed a language-independent lstm architecture to identify vmwes, which includes both convolutional and recurrent layers, and an optional high-level crf layer. additionally, @xcite focused on using mwes to identify verbal metaphors and proposed a deep learning model based on attention-guided gcns, which incorporate both syntactic dependencies and information about vmwes. supervised techniques like deep learning require vast amounts of labeled data. the fine-tuning step of the bert model has the capability to tackle this issue, making it a powerful tool. parsbert, developed by @xcite , is a monolingual persian language model based on google's bert architecture that utilizes the same bert-base settings. it was trained on over 2 million diverse documents, allowing it to perform various tasks, including sentiment analysis, text classification, and named entity recognition. ## vmwe identification in this section, we first present the datasets utilized in the proposed approach for vmwe identification, followed by a detailed description of the methods and models employed for this task. to detect vmwes, a combination of a non-contextual method and two deep learning models are employed. these deep learning models treat the vmwe detection task as a sequence labelling problem, where the goal is to assign a relevant tag to each token in the sequence. to accomplish this, an iob-like labelling format was used to tag the vmwes in sentences, where the beginning component of the expression is tagged as 'b', its other components are tagged as 'i', and the words in the sentence that do not belong to any vmwe receive an 'o' tag. additionally, sentences containing two vmwes with mixed components were removed for simplicity (e.g. 5). the two deep learning models used are an lstm-based architecture and a bert-based model. (5) ‫زندگ‬ ‫طول‬ ‫تمام‬ ‫در‬ ‫ی‬ ‫باز‬ ‫نقش‬ ‫اش‬ ‫ی‬ ‫کرد‬ lit. in all length his life role play did he impersonated during all his life vmwe1 : ‫ب‬ ‫ازی‬ ‫کرد‬ (play @xmath0: ‫باز‬ ‫نقش‬ ‫ی‬ ‫کرد‬ (role play @xmath1 ## predicting the compositionality of vmwes the primary objective of this paper is to predict the compositionality of vmwes. our assumption is that the degree of compositionality of a multiword expression can be determined by evaluating the semantic similarity between its constituent components and the expression itself. this evaluation is conducted by comparing the similarity of the embedding vectors of the corresponding word tokens. to accomplish this, we follow the studies of @xcite and @xcite and investigate six metrics to determine the compositionality of vmwes. in this section, the criteria for the task and a description of the datasets are presented. ## results and discussion this section showcases the evaluation outcomes achieved during the testing phase for identifying vmwes and predicting their compositionality. the evaluation was performed on the parseme corpus test-set for all identification techniques. ## conclusion to conclude, this paper presented an approach to predicting the compositional nature of vmwes in persian. the proposed method utilized automatic identification of vmwes, followed by the creation of word embeddings that better capture the semantic properties of these expressions, and multiple criteria to determine their degree of compositionality. the study compared two neural architectures, bilstm and parsbert, and found that a fine-tuned bert model outperformed the bilstm model with an f1 score of 89%. moreover, the paper demonstrated the effectiveness of a word2vec embedding model in capturing the semantics of identified vmwes and used criteria, resulting in an accuracy of 70.9% on a collected dataset of expert-annotated compositional and non-compositional vmwes. these findings have important implications for further research in predicting the compositional nature of multiword expressions.