The University of Pittsburgh English Language Institute Corpus (PELIC)

Juffs, Alan; Han, Na-Rae; Naismith, Ben

doi:10.5281/zenodo.3991977

Cited by 3 publications

(4 citation statements)

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“…The two corpora analysed are COCA (The Corpus of Contemporary American English; Davies, 2008Davies, -, 2018 and PELIC (The University of Pittsburgh English Language Institute Corpus; Juffs, Han & Naismith, 2020). Rather than querying through a web browser interface, all data files were stored and processed locally on a personal computer using Python and R programing languages in Jupyter notebooks.…”

Section: Methodology 1 Datasetmentioning

confidence: 99%

“…COCA = Corpus of Contemporary American English (Davies, 2008(Davies, -, 2018. PELIC = The University of Pittsburgh English Language Institute Corpus (Juffs, Han & Naismith, 2020). Forty-six items were mis-tagged due to learner language as particles (40), prepositions (5), and pre-determiners (1) and were not included in this table.…”

Section: Findings 1 Use Of 'Key' Word Familiesmentioning

confidence: 99%

“…COCA = Corpus of Contemporary American English(Davies, 2008(Davies, -, 2018. PELIC = The University of Pittsburgh English Language Institute Corpus(Juffs, Han & Naismith, 2020).…”

mentioning

confidence: 99%

“…Notes. PELIC = The University of Pittsburgh English Language Institute Corpus(Juffs, Han & Naismith, 2020).…”

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confidence: 99%

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Finding the sweet spot: Learners’ productive knowledge of mid-frequency lexical items

Naismith

Juffs

2021

Language Teaching Research

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Research into vocabulary knowledge often differentiates between breadth (how many words a person knows) and depth (how well the words are known). Both theoretical categories are essential for understanding language learners’ lexical development, but how the different aspects of vocabulary knowledge interconnect has not received the same attention as each individual dimension, especially in terms of productive knowledge. This study analyses lexis from mid-frequency lemmas in the K3–K9 frequency bands from the learner corpus PELIC (The University of Pittsburgh English Language Corpus). Critically for learners, mastery of lexis in this frequency range is essential for achieving the English proficiency required for university study. From these mid-frequency items, a dataset of 7,554 tokens were collected from word families with multiple derivations and manually annotated. The findings showed high rates of collocational and derivational accuracy for the forms learners opted to use. However, compared to expert speaker texts in the Corpus of Contemporary American English (COCA), learners overused the verb forms and underused the noun forms of these lexical items. These patterns provide evidence of the interplay between breadth and depth in learners’ productive vocabulary usage, suggesting that increased lexical depth will naturally lead to greater lexical breadth and vice versa. Pedagogical implications reaffirm the importance of developing learners’ explicit morphological awareness and collocational accuracy. Suggestions for mid-frequency lexical items to prioritize in language learning are also provided, with a view to helping learners achieve academic readiness.

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Section: Methodology 1 Datasetmentioning

confidence: 99%

Section: Findings 1 Use Of 'Key' Word Familiesmentioning

confidence: 99%

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Finding the sweet spot: Learners’ productive knowledge of mid-frequency lexical items

Naismith

Juffs

2021

Language Teaching Research

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Second language learning of degree expressions: A computational approach

Cong

2024

Nat. lang. processing

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Degrees, unlike entities or events, refer to comparative qualities and are closely tied to gradable adjectives such as “tall.” Degree expressions have been explored in second language (L2) research, covering areas such as learnability, first language (L1) transfer, contrastive analysis, and acquisition difficulty. However, a computational approach to the learning of degree expressions in L2 contexts, particularly for L1 Chinese learners of English, has not been thoroughly investigated. This study aims to fill this gap by utilizing natural language processing (NLP) methods, drawing insights from recent advancements in large language models (LLMs). This study extends Cong (2024)’s general-purpose assessment pipeline to specifically analyze degree expressions, predicting that surprisal metrics will correlate with proficiency levels and distinct developmental stages of L2 learners. Crucially, we address the limitations of surprisal metrics in capturing underuse or avoidance—common in L2 development—by integrating frequency-based analyses. Using an NLP pipeline developed with Stanza, we automatically identified and analyzed degree expressions, constructing linear mixed-effects models to track L2 development trajectories. Our findings reveal that as proficiency increases, learners use complex degree expressions more frequently, supporting theories linking difficulty and learnability. Higher surprisal values are associated with lower proficiency in using degree expressions, and these surprisals are more predictive of degree expressions proficiency than classic NLP measures. These results add further evidence that LLMs and NLP tools provide valuable insights into L2 development, specifically in the domain of degree expressions, expanding upon previous research and offering new approaches for understanding L2 learning processes.

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The University of Pittsburgh English Language Institute Corpus (PELIC)

Naismith

Han

Juffs

2022

IJLCR

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This report introduces the University of Pittsburgh English Language Institute Corpus (PELIC; Juffs et al., 2020), a publicly available 4.2-million-word learner corpus of written texts. Collected over seven years in the University of Pittsburgh’s Intensive English Program, these texts were produced by more than 1,100 students with diverse linguistic backgrounds and proficiency levels. Unlike most learner corpora which are cross-sectional, PELIC is longitudinal, offering greater opportunities for tracking development in a natural classroom setting. This potential is illustrated in an overview of the research conducted to date with these data. The report also provides a description of PELIC’s creation and contents, including how the texts have been managed to facilitate natural language processing. Overall, the corpus contributes to the field of learner corpus research by adding to the pool of freely and publicly available learner corpora, supplemented by a useful set of Python tools and tutorials for accessing these data.

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The University of Pittsburgh English Language Institute Corpus (PELIC)

Cited by 3 publications

References 0 publications

Finding the sweet spot: Learners’ productive knowledge of mid-frequency lexical items

Finding the sweet spot: Learners’ productive knowledge of mid-frequency lexical items

Second language learning of degree expressions: A computational approach

The University of Pittsburgh English Language Institute Corpus (PELIC)

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