Discovery of Multiword Expressions with Loanwords and Their Equivalents in the P ersian Language (2021)

Abstract: This paper presents an attempt at multiword expressions (MWEs) discovery in the Persian language. It focuses on extracting MWEs containing lemmas of a particular group: loanwords in Persian and their equivalents proposed by the Academy of Persian Language and Literature. In order to discover such MWEs, four association measures (AMs) are used and evaluated. Finally, the list of extracted MWEs is analyzed, and a comparison between expressions with loanwords and equivalents is presented. To our knowledge, this is the first time such analysis was provided for the Persian language.

## introduction today, almost 19 years after the seminal paper "multiword expressions -a pain in the neck for nlp" by @xcite , multiword expressions (mwes) are still an interesting and challenging aspect of many natural language processing (nlp) tasks, which is reflected in the number of papers addressing this phenomenon as well as the number of people contributing to, and attending workshops, conferences, and initiatives such as siglex-mwe foot_0 or parseme. 2 mwes are very frequent in language and range over a number of different linguistic constructions, from idioms, e.g., to pay an arm and a leg, to fixed expressions, e.g., rock and roll, light verb constructions, e.g., take a shower, to noun compounds, e.g., golf club. @xcite claim that the number of mwes in spoken english is 30% -45% and 21% in academic prose. @xcite suggests that the number of mwes in a speaker's lexicon is the same as simple words. nevertheless, if we take into consideration the domain-specific lexicons, this number seems to be an underestimation @xcite . indeed, the research conducted by @xcite suggests that the mwes ratio can be between 50% and 80% in a corpus of scientific biomedical abstracts. research by @xcite estimate that mwes can constitute more than 70% of the specialized lexicon. mwes have received considerable attention in recent years and it has been suggested @xcite that their proper treatment could make a significant improvements in a number of nlp tasks, e.g., lexicography @xcite @xcite , word sense disambiguation @xcite , part-ofspeech tagging and parsing @xcite , information retrieval @xcite , language learning @xcite , machine translation @xcite or sentiment analysis @xcite . the research on mwes in persian has so far focused mainly on verbal multiword units and light verb constructions (lvcs) in particular. @xcite adopted a method originally proposed by @xcite for identifying lvcs in persian. they extended existing statistical measures of the acceptability of english lvcs, used semantic classes of nouns, and proved that semantic class information is useful for lvc acceptability of new combinations. @xcite used bilingual parallel corpus (persian-english) and investigated the usefulness of several linguisticallyinformed features for automatic identification of persian lvcs. persian (among 18 other languages) and the analysis of its verbal mwes have also been addressed as part of the parseme shared task on automatic identification of verbal mwes @xcite . the best system for persian in the task obtained an outstanding f-score, which exceeds 0.9. the reasons for such a high f-score can be perceived in two factors: the density of light verbs is exceptionally high in persian, and the information about lvcs was contained in morphological companion files. @xcite , on the other hand, do not focus on any particular mwe type but rather try to cover the whole spectrum of mwe types. their model is trained on a treebank with mwe relations of a source language applied to a corpus of a surprise language to identify its mwe construction types. this paper presents an evaluation of four association measures used for the extraction of persian multiword expressions with loanwords and their native counterparts. farhangestan-e zaban va adab-e farsi ('academy of persian language and literature') is an official body responsible for the persian language, its resources, and reforms. one of the tasks of the academy is to propose persian equivalents for borrowed terms. so far, the academy has been successful in issuing thirteen lists of "collection of terms approved". these are, as the name suggests, terms that the speakers of persian should use. the total number of approved terms is more than 45,000. they are also available on academy's website @xcite . this study aims at 1) applying ams to extract mwes and 2) comparing ten loanwords and their equivalents to evaluate their potential to form mwes. the rest of this paper is organized as follows. section 2 presents information on work related to association measures used for mwes discovery. methodology is presented in section 3. it describes the corpus used in this study, lemmas selected as initial seeds, and the four association measures. the results and their evaluation are addressed in section 4. finally, the conclusion and plans for future work are presented in section 5. ## related work the assumption that mwes stand out, i.e., they exhibit some sort of sailence, allows to extract (or discover) them automatically from texts. this salience is also the reason why especially statistical measures have been so popular when it comes to the discovery of multiword expressions. many studies indicate that words that tend to co-occur more frequently than by a pure chance are good candidates for mwes and propose detecting this statistical significance by measuring the association strength between these words @xcite @xcite . such statistical metrics that can estimate the relationship strength between words in a corpus, based on these words' co-occurrence count and their individual word counts, are known as association measures (ams). since mwes are characterized by strong collocational behavior, statistical association measures have been widely used in mwes discovery. the number of proposed association measures over the years has been impressive. more than 30 ams were described by @xcite , @xcite presented a list with over 80 and new measures as well as their variants are constantly being proposed @xcite . however, although numerous studies propose and experiment with association measures performance, there is no consensus on which metric is best for extracting mwes. @xcite mentioned that although some measures are more popular and have become standards (e.g., pointwise mutual information, loglikelihood, or t-score), the choice of a suitable metric depends on the particular task as each measure focuses on a different aspect of collocation strength. since different measures capture various aspects of mwes, @xcite proposed combining some of them and showed that when in combination, ams can generate better results for mwe discovery than if used in isolation. the most widely used association measure for mwe discovery is the pointwise mutual information (pmi) proposed by @xcite for terminology discovery. it is derived for bigrams directly from the mutual information between two random variables, using the log-ratio between the observed co-occurrences of the sequence and the individual words to determine how much the cooccurrence is due to mutual preference. the reported issue with pmi is that it is biased towards infrequent events @xcite . therefore, as observed by @xcite a moderately associated low-frequency bigram might obtain a better score than a highly associated high-frequency bigram. another popular group of ams used for mwe discovery is based on hypothesis testing. assuming the null hypothesis that words are independent, their observed and expected counts should be the same. large values indicate that the candidate words are not independent and can potentially form a mwe. examples of hypothesis-based ams are t-score and z-score. they are both based on the assumption of normal distribution, and they work well for frequent events. however, their usage is not recommended for low-frequency pairs. the z-score test is also not suited for small corpora @xcite . ams based on contingency tables record the marginal frequencies of the words in an n-gram and the probability of their non-co-occurrence. one such measure is pearson's chi-squared test (χ 2 ) which overcomes the normal distribution problem as it makes no data assumptions. however, χ 2 is again not recommended for small corpora (manning and schütze, 1999), and it also tends to prefer common events @xcite . another example is log-likelihood ratio @xcite ) -a well-known association measure for collocation extraction. it performs well with both frequent and rare events as well as different corpora sizes @xcite . however, its reliability is affected by low values of expected frequencies in the contingency table @xcite . although ams have a long history and their utility have been sometimes questioned (e.g., @xcite , they are still sucessfully used in extraction systems, e.g., @xcite , @xcite , @xcite . they also remain an important part of other approaches to mwes discovery, e.g., @xcite , @xcite or @xcite . ## conclusion in this paper, an approach to the discovery of persian mwes was presented. we focused on a particular group of mwes: constructions including loanwords in persian and their native equivalents proposed by the official iranian body responsible for language reforms -the academy of persian language and literature. the extraction of mwes was performed with the use of four popular association measures. there were two goals of this study: 1) to evaluate the performance of association measures for the discovery of mwes in persian, and 2) to compare and analyze mwes with loanwords and mwes with equivalents. the former goal was achieved for the four most popular association measures, with t-score performing best with loanword mwes and pmi with equivalent ones. to our knowledge, it is the first time such analysis was carried out to discover persian mwes. the evaluation of mwes with loanwords and their persian equivalents was performed for ten pairs, providing information on shared mwes as well as distinct ones. the complete list of extracted mwes will be available for translators and students of the persian language. future work includes exploiting a bigger number of association measures and other approaches to mwes discovery. moreover, we would like to investigate the impact of genres and context on forming distinct mwes with loanwords and equivalents.