Automatic Generation of Vocabulary Lists with Multiword Expressions (2023)

Abstract: The importance of multiword expressions (MWEs) for language learning is well established. While MWE research has been evaluated on various downstream tasks such as syntactic parsing and machine translation, its applications in computer-assisted language learning has been less explored. This paper investigates the selection of MWEs for graded vocabulary lists. Widely used by language teachers and students, these lists recommend a language acquisition sequence to optimize learning efficiency. We automatically generate these lists using difficulty-graded corpora and MWEs extracted based on semantic compositionality. We evaluate these lists on their ability to facilitate text comprehension for learners. Experimental results show that our proposed method generates higher-quality lists than baselines using collocation measures.

## introduction effective processing of multiword expressions (mwes) is critical for many natural language processing (nlp) applications. in addition to intrinsic evaluation on the quality of extracted mwes, researchers have conducted extrinsic evaluation to measure their impact on syntactic parsing, machine translation and other tasks @xcite . however, mwe extraction methods have not yet been evaluated in generating vocabulary lists, even though the importance of mwes, which may require idiosyncratic interpretations, is well established for language learning @xcite . graded vocabulary lists recommend a language acquisition sequence for language learners and teachers, in order to optimize learning efficiency of the target language. these lists help prioritize words and expressions that are more likely to be encountered by learners, so that they can understand more texts within a shorter period of study. according to @xcite , the number of mwes in a speaker's lexicon has been estimated to be of the same order of magnitude as the number of single words @xcite . it is no surprise, then, that a significant number of mwes are included in prominent vocabulary lists such as english vocabulary profile (evp) foot_0 and the pearson global scale of english (gse). 2 we investigate the selection of mwes for graded vocabulary lists, assuming only a graded corpus for n-gram statistics and large general corpora for mwe extraction. to the best of our knowledge, this is the first evaluation on corpus-based methods for generating vocabulary lists with mwes. the rest of the paper is organized as follows. after reviewing previous research (section 2), we present our datasets (section 3) and evaluation metrics (section 4). we then describe our approach (section 5) and report experimental results (section 6). 3 ## previous work the research most closely related with ours is efllex, a vocabulary list for learners of english as a foreign language @xcite . it contains both single words and mwes, including compounds and phrasal verbs. a rule-based method identifies the mwes by considering the dependency labels and verb particles in parse trees of sentences in a large collection of english corpora, followed by manual checking. while cefrlex resources have been found to be effective in predicting the cefr levels of the efllex entries @xcite , mwes have not been evaluated. several other popular vocabulary lists, such as the new general service list 4 and the oxford lists 5 , do not feature mwes and therefore are not comparable list # single # bigram # trigram words mwes mwes evp 6,749 993 839 gse 18,391 2,821 1085 efllex 10,019 3,745 106 in addition to efllex, we also evaluate a recently proposed mwe extraction method based on unsupervised measurement of semantic compositionality @xcite . this method first identifies bigrams and trigrams as mwe candidates using the poisson collocation measure @xcite . it then ranks these candidates according to the average cosine similarity between the word vector of the mwe candidate and the word vector of each of its constituent words. experimental results show that the use of word2vec embeddings can achieve substantial correlation with human judgment. ## evaluation methodology our evaluation focuses on mwes up to trigrams only, since longer ones are not available in the dataset from @xcite . let @xmath0, . . . , s k } represent a graded vocabulary list with k grades, where s i is the set of n-grams (@xmath1. all n-grams are in lemma form. the benchmark vocabulary lists adopt different numbers of grades and lemmas. we transform each list into a single ranked list (section 4.1) to facilitate a fair evaluation (section 4.2). ## approach mwes may include fixed and semi-fixed expressions, syntactically-flexible expressions and institutionalized phrases @xcite . as shown in frequency all n-grams (@xmath2.1) are considered as singleword and mwe candidates for the vocabulary list. they are lemmatized and ranked them according to frequency in the training corpora. collocation same as the above, except that the mwe candidates are the top 500,000 n-grams in english wikipedia based on the poisson collocation measure (quasthoff and wolff, 2002). 9 compositionality(n%) among the 500,000 mwe candidates above, this method retains as candidates only the top n % according to the semantic compositionality measure (section 2). efllex the mwe candidates are those found in efllex @xcite . in addition, we implemented the following method to gauge the upper limit in performance: compositionality(gold) the mwe candidates are the gold mwes. evp / gse the expert-crafted lists, transformed into a ranked list using the procedure in section 4.1. ceiling the mwe candidates are the gold mwes and all n-grams are ranked by frequency in the test set (section 3.1). ## conclusion this paper has presented the first corpus-based evaluation of automatically generated vocabulary lists that incorporate mwes. using mwes extracted by semantic compositionality @xcite , we constructed a vocabulary list by ranking both singleword and mwe candidates by frequency and dispersion. experimental results show that this method outperforms baselines using collocation measures, both in facilitating text comprehension and in shortening the study period. these algorithms can potentially enhance existing humancrafted lists, and compile new ones in resourcepoor languages for which no vocabulary list is available.