Automatic extraction of subordinate clauses and its application in second language acquisition research.

Clause subordination is an important linguistic phenomenon that is relevant to research in psycholinguistics, cognitive and behavioral sciences, language acquisition, and computational information retrieval. The paper presents a comprehensive tool called AutoSubClause, which is specifically designed for extracting subordinate clause (SC) information from natural English production. Using dependency parsing, AutoSubClause is able to extract not only information characterizing the three main types of SCs-complement, adverbial, and relative clauses-but also information regarding the internal structure of different clause types and their semantic and structural relations with elements of the main clause. Robustness testing of the system and its underlying dependency parser Stanford CoreNLP showed satisfactory results. To demonstrate the usefulness of AutoSubClause, we used it to analyze a large-scale learner corpus and investigate the effects of first language (L1) on the acquisition of subordination in second language (L2) English. Our analysis shows that learners from an L1 that is typologically different from the L2 in clause subordination tend to have different developmental trajectories from those whose L1 is typologically similar to the L2. Furthermore, the developmental patterns for different types of SCs also vary. This finding suggests the need to approach clausal subordination as a multi-componential construct rather than a unitary one, as is the case in most previous research. Finally, we demonstrate how NLP technology can support research questions that rely on linguistic analysis across various disciplines and help gain new insights with the increasing opportunities for up-scaled analysis.

Examining the potential influence of crosslinguistic lexical similarity on word-choice transfer in L2 English.

We examined whether and how L1-L2 crosslinguistic formal lexical similarity influences L2 word choice. Our sample included two learner subcorpora, containing 8,500 and 6,390 English texts, written in an educational setting, by speakers of diverse L1s in the A1-B2 CEFR range of L2 proficiency. We quantified similarity based on phonological overlap between L1 words and their L2 (English) translations. This similarity relates to psycholinguistic cognancy, which occurs when words and their translations share a high level of formal similarity, often due to historical cognancy from shared etymology or language contact. We then used mixed-effects statistical models to examine how this similarity influences the rate of use of the L2 words; essentially, we checked whether L2 words that are more similar to their L1 translations are used more often. We also controlled for potential confounds, including the baseline L1 frequency of the English words. The type of crosslinguistic similarity that we examined did not influence learners' choice of L2 words in their writing in the present sample, which represents a type of educational setting that many learners encounter. This suggests that the influence of such similarity is constrained, and that communicative needs can override transfer from learners' L1 to their L2, which raises questions regarding when and how else situational factors can influence transfer.