TY - JOUR
T1 - Predicting lexical proficiency in language learner texts using computational indices
AU - Crossley, Scott A.
AU - Salsbury, Tom
AU - McNamara, Danielle S.
AU - Jarvis, Scott
N1 - Funding Information:
This research was supported in part by the Institute for Education Sciences (IES R305A080589 and IES R305G20018-02). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the IES. We also give special thanks to our human raters: Brandi Williams, Jessica Mann, and Abigail Voller. We would also like to acknowledge the assistance of Dr. Phillip McCarthy in helping validate the scoring rubric used in this study.
PY - 2011/10
Y1 - 2011/10
N2 - The authors present a model of lexical proficiency based on lexical indices related to vocabulary size, depth of lexical knowledge, and accessibility to core lexical items. The lexical indices used in this study come from the computational tool Coh-Metrix and include word length scores, lexical diversity values, word frequency counts, hypernymy values, polysemy values, semantic co-referentiality, word meaningfulness, word concreteness, word imagability, and word familiarity. Human raters evaluated a corpus of 240 written texts using a standardized rubric of lexical proficiency. To ensure a variety of text levels, the corpus comprised 60 texts each from beginning, intermediate, and advanced second language (L2) adult English learners. The L2 texts were collected longitudinally from 10 English learners. In addition, 60 texts from native English speakers were collected. The holistic scores from the trained human raters were then correlated to a variety of lexical indices. The researchers found that lexical diversity, word hypernymy values and content word frequency explain 44% of the variance of the human evaluations of lexical proficiency in the examined writing samples. The findings represent an important step in the development of a model of lexical proficiency that incorporates both vocabulary size and depth of lexical knowledge features.
AB - The authors present a model of lexical proficiency based on lexical indices related to vocabulary size, depth of lexical knowledge, and accessibility to core lexical items. The lexical indices used in this study come from the computational tool Coh-Metrix and include word length scores, lexical diversity values, word frequency counts, hypernymy values, polysemy values, semantic co-referentiality, word meaningfulness, word concreteness, word imagability, and word familiarity. Human raters evaluated a corpus of 240 written texts using a standardized rubric of lexical proficiency. To ensure a variety of text levels, the corpus comprised 60 texts each from beginning, intermediate, and advanced second language (L2) adult English learners. The L2 texts were collected longitudinally from 10 English learners. In addition, 60 texts from native English speakers were collected. The holistic scores from the trained human raters were then correlated to a variety of lexical indices. The researchers found that lexical diversity, word hypernymy values and content word frequency explain 44% of the variance of the human evaluations of lexical proficiency in the examined writing samples. The findings represent an important step in the development of a model of lexical proficiency that incorporates both vocabulary size and depth of lexical knowledge features.
KW - computational linguistics
KW - corpus linguistics
KW - depth of lexical knowledge
KW - hypernymy
KW - lexical diversity
KW - lexical frequency
KW - lexical proficiency
KW - vocabulary size
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U2 - 10.1177/0265532210378031
DO - 10.1177/0265532210378031
M3 - Article
AN - SCOPUS:79952752042
SN - 0265-5322
VL - 28
SP - 561
EP - 580
JO - Language Testing
JF - Language Testing
IS - 4
ER -