172011372 03 01 01 JB code JB John Benjamins Publishing Company 01 JB code SiBil 47 Eb 15 9789027271679 06 10.1075/sibil.47 00 EA E107 10 01 JB code SiBil 02 0928-1533 02 47.00 01 02 Studies in Bilingualism Studies in Bilingualism 11 01 JB code jbe-all 01 02 Full EBA collection (ca. 4,200 titles) 11 01 JB code jbe-2015-all 01 02 Complete backlist (3,208 titles, 1967–2015) 05 02 Complete backlist (1967–2015) 11 01 JB code jbe-2015-sibil 01 02 Studies in Bilingualism (vols. 1–48, 1991–2015) 05 02 SIBIL (vols. 1–48, 1991–2015) 11 01 JB code jbe-2015-linguistics 01 02 Subject collection: Linguistics (2,773 titles, 1967–2015) 05 02 Linguistics (1967–2015) 11 01 JB code jbe-2015-psychology 01 02 Subject collection: Psychology (246 titles, 1978–2015) 05 02 Psychology (1978–2015) 01 01 Vocabulary Knowledge Human ratings and automated measures Vocabulary Knowledge: Human ratings and automated measures 1 B01 01 JB code 509185040 Scott Jarvis Jarvis, Scott Scott Jarvis Ohio University 07 https://benjamins.com/catalog/persons/509185040 2 B01 01 JB code 116185041 Michael Daller Daller, Michael Michael Daller Swansea University, Wales, UK 07 https://benjamins.com/catalog/persons/116185041 01 eng 11 228 03 03 viii 03 00 220 03 01 23 418.0071 03 2013 P53.9 04 Vocabulary--Ability testing. 04 Language and languages--Ability testing. 10 LAN009000 12 CFDM 24 JB code LIN.COMPUT Computational & corpus linguistics 24 JB code LIN.LA Language acquisition 24 JB code LIN.BIL Multilingualism 24 JB code LIN.PSYLIN Psycholinguistics 01 06 02 00 A collection of the latest advances, developments, and innovations regarding the modeling and measurement of learners’ vocabulary growth curves, current levels of vocabulary knowledge and lexical proficiency, and the patterns of lexical diversity found in their language production. 03 00 Language researchers and practitioners often adopt tools and techniques without testing whether they really work as they should. This is understandable because most scholars do not have the time or expertise to properly evaluate the usefulness of all instruments, measures, and methods they need. It is therefore critical to have problem solvers in the field who gain the necessary expertise and take the time to scrutinize existing methods, identify problems, and offer new solutions. This volume represents the work of scholars who have done this; it is a collection of the latest advances, developments, and innovations regarding the modeling and measurement of learners’ vocabulary growth curves, current levels of vocabulary knowledge and lexical proficiency, and the patterns of lexical diversity found in their language production. Several of the contributors also address the complex but important relationship between automated indices and human judgments of learners’ lexical patterns and abilities. 01 00 03 01 01 D503 https://benjamins.com/covers/475/sibil.47.png 01 01 D502 https://benjamins.com/covers/475_jpg/9789027241887.jpg 01 01 D504 https://benjamins.com/covers/475_tif/9789027241887.tif 01 01 D503 https://benjamins.com/covers/1200_front/sibil.47.hb.png 01 01 D503 https://benjamins.com/covers/125/sibil.47.png 02 00 03 01 01 D503 https://benjamins.com/covers/1200_back/sibil.47.hb.png 03 00 03 01 01 D503 https://benjamins.com/covers/3d_web/sibil.47.hb.png 01 01 JB code sibil.47.01aut 06 10.1075/sibil.47.01aut vii viii 2 Miscellaneous 1 01 04 Bio data of authors Bio data of authors 01 eng 01 01 JB code sibil.47.02int 06 10.1075/sibil.47.02int 1 12 12 Article 2 01 04 Introduction Introduction 1 A01 01 JB code 430191620 Scott Jarvis Jarvis, Scott Scott Jarvis 07 https://benjamins.com/catalog/persons/430191620 2 A01 01 JB code 682191621 Michael Daller Daller, Michael Michael Daller 07 https://benjamins.com/catalog/persons/682191621 01 eng 01 01 JB code sibil.47.03ch1 06 10.1075/sibil.47.03ch1 13 44 32 Chapter 3 01 04 Chapter 1. Defining and measuring lexical diversity Chapter 1. Defining and measuring lexical diversity 1 A01 01 JB code 93191622 Scott Jarvis Jarvis, Scott Scott Jarvis 07 https://benjamins.com/catalog/persons/93191622 01 eng 03 00

Most existing measures of lexical diversity are either direct or indirect measures of the proportion of repeated words in a language sample, and they tend to be validated in accordance with how well they avoid sample-size effects and/or how strongly they correlate with measures of knowledge and proficiency. The present paper argues that such measures suffer from the lack of construct validity in two ways: (a) They are not grounded in an adequate or clearly articulated theoretical account of the nature of the construct of lexical diversity, and (b) they are not validated in relation to how well they measure lexical diversity itself, but rather in relation to how well they do or do not correlate with other constructs. The present paper proposes solutions to both of these problems by defining lexical diversity as a perception-based phenomenon with six measurable properties, and by calibrating the six objective properties against human judgments of lexical diversity. The purpose of the empirical portion of the paper is to determine how well a statistical model constructed on the basis of the proposed six objective properties is able to account for nine human raters’ judgments of the lexical diversity found in 50 narratives written by adolescent learners and native speakers of English. The results support the proposed six-dimensional construct of lexical diversity, but also suggest the need for further refinements to how the six properties are measured.

01 01 JB code sibil.47.04ch2 06 10.1075/sibil.47.04ch2 45 78 34 Chapter 4 01 04 Chapter 2. From intrinsic to extrinsic issues of lexical diversity assessment Chapter 2. From intrinsic to extrinsic issues of lexical diversity assessment 01 04 An ecological validation study An ecological validation study 1 A01 01 JB code 313191623 Philip McCarthy McCarthy, Philip Philip McCarthy Decooda International and Ohio University 07 https://benjamins.com/catalog/persons/313191623 2 A01 01 JB code 721191624 Scott Jarvis Jarvis, Scott Scott Jarvis Decooda International and Ohio University 07 https://benjamins.com/catalog/persons/721191624 01 eng 03 00

Issues of lexical diversity assessment have only been addressed with consideration of the approach, rather than the corpus. Of necessity, intrinsic issues of lexical diversity related to the approach needed to be addressed first; however, given that they now have received due attention in recent research, it is time to turn our attention to extrinsic issues of lexical diversity, which is the assessment of how variations in texts and corpora affect the results of the approach. The focus of intrinsic issues has been on the algorithms and approaches used to produce values of lexical diversity on laboratory-like data sets. With extrinsic issues of word count, the focus moves to more naturalistic data sets with texts that demonstrate ranges of inconsistencies in terms of size, quality, and length. For these data, indices of lexical diversity are required to demonstrate ecological validity. The degree to which an index of lexical diversity exhibits ecological validity is of considerable importance to the field of second language learning because naturalistic corpora vary considerably in size, and texts within the corpora vary considerably in terms of word count. In other words, ecological validity is a necessary element of the construct validity of lexical diversity. In this study, we assess the three primary indices of lexical diversity (MTLD, HD-D, and Maas) using a corpus of naturalistic data in order to evaluate extrinsic issues of lexical diversity assessment by way of ecological validation. Our results show that the index of MTLD appears strongest and the index of Maas appears the least strong. Our conclusion, while encouraging broader research, is that the Maas index be abandoned as a lexical diversity index because of its over-sensitivity to word count. By contrast, MTLD appears to be resilient to a wide range of extrinsic factors and, consequently, is recommended for future lexical diversity studies.

01 01 JB code sibil.47.05ch3 06 10.1075/sibil.47.05ch3 79 104 26 Chapter 5 01 04 Chapter 3. Measuring lexical diversity among L2 learners of French Chapter 3. Measuring lexical diversity among L2 learners of French 01 04 An exploration of the validity of D, MTLD cand HD-D as measures of language ability An exploration of the validity of D, MTLD 
and HD-D as measures of language ability 1 A01 01 JB code 897191625 Jeanine Treffers-Daller Treffers-Daller, Jeanine Jeanine Treffers-Daller University of Reading 07 https://benjamins.com/catalog/persons/897191625 01 eng 03 00

In this study two new measures of lexical diversity are tested for the first time on French. The usefulness of these measures, MTLD (McCarthy and Jarvis (2010 and this volume) and HD-D (McCarthy and Jarvis 2007), in predicting different aspects of language proficiency is assessed and compared with D (Malvern and Richards 1997; Malvern, Richards, Chipere and Durán 2004) and Maas (1972) in analyses of stories told by two groups of learners (n = 41) of two different proficiency levels and one group of native speakers of French (n = 23). The importance of careful lemmatization in studies of lexical diversity which involve highly inflected languages is also demonstrated. The paper shows that the measures of lexical diversity under study are valid proxies for language ability in that they explain up to 62 percent of the variance in French C-test scores, and up to 33 percent of the variance in a measure of complexity. The paper also provides evidence that dependence on segment size continues to be a problem for the measures of lexical diversity discussed in this paper. The paper concludes that limiting the range of text lengths or even keeping text length constant is the safest option in analysing lexical diversity.

01 01 JB code sibil.47.06ch4 06 10.1075/sibil.47.06ch4 105 134 30 Chapter 6 01 04 Chapter 4. Validating lexical measures using human scores of lexical proficiency Chapter 4. Validating lexical measures using human scores of lexical proficiency 1 A01 01 JB code 352191626 Scott A. Crossley Crossley, Scott A. Scott A. Crossley Georgia State University 07 https://benjamins.com/catalog/persons/352191626 2 A01 01 JB code 301191627 Tom Salsbury Salsbury, Tom Tom Salsbury Washington State University 07 https://benjamins.com/catalog/persons/301191627 3 A01 01 JB code 619191628 Danielle S. McNamara McNamara, Danielle S. Danielle S. McNamara Arizona State University 07 https://benjamins.com/catalog/persons/619191628 01 eng 03 00

This study examines the convergent validity of a wide range of computational indices reported by Coh-Metrix that have been associated in past studies with lexical features such as basic category words, semantic co-referentiality, word frequency, and lexical diversity. This study uses human judgments of these lexical features as found in free-writing samples as operationalizations of the lexical constructs the indices are meant to measure. Statistical analyses were then conducted to examine the convergent validity of each index and to assess the predictive ability of the indices that correlate strongest with the human judgments to explain holistic scores of lexical proficiency in L1 and L2 speakers. Correlations between the automated lexical indices and the operationalized constructs demonstrated small to large effect sizes providing a degree of convergent validity for most of the automated indices examined in this study. A multiple regression predicting holistic judgments of lexical proficiency using these automated lexical indices explained 40% of the variance in a training set and 37% of the variance in a test set. The findings from the study provide a degree of confidence that the indices are measuring the constructs they were predicted to measure.

01 01 JB code sibil.47.07ch5 06 10.1075/sibil.47.07ch5 135 156 22 Chapter 7 01 04 Chapter 5. Computer simulations of MRC Psycholinguistic Database word properties Chapter 5. Computer simulations of MRC Psycholinguistic Database word properties 01 04 Concreteness, familiarity, and imageability Concreteness, familiarity, and imageability 1 A01 01 JB code 967191629 Scott A. Crossley Crossley, Scott A. Scott A. Crossley Georgia State University 07 https://benjamins.com/catalog/persons/967191629 2 A01 01 JB code 279191630 Shi Feng Feng, Shi Shi Feng University of Memphis 07 https://benjamins.com/catalog/persons/279191630 3 A01 01 JB code 595191631 Zhiqiang Cai Cai, Zhiqiang Zhiqiang Cai University of Memphis 07 https://benjamins.com/catalog/persons/595191631 4 A01 01 JB code 667191632 Danielle S. McNamara McNamara, Danielle S. Danielle S. McNamara Arizona State University 07 https://benjamins.com/catalog/persons/667191632 01 eng 03 00

This study investigates the potential for computational models informed through automated lexical indices to simulate human ratings of word concreteness, word familiarity, and word imageability. The goal of the study is to provide word information estimates for words with human ratings, thereby affording greater textual coverage and permitting a better understanding of features that underlie word properties. This study uses traditional automated word features such word length, word frequency, hypernymy, and polysemy along with novel automated word features such as word type attributes taken from WordNet, LSA dimensions, and inverse entropy weights as predictor variables. The model reported in this study for word concreteness predicted 61% of the variance in human ratings of word concreteness and demonstrated that more concrete words contain attributes related to people, animals, and food, have higher hypernymy levels, are related to two LSA dimensions, are more frequent, and are shorter. The model for word familiarity predicted 62% of the variance in the human ratings reported in the MRC database and demonstrated that more familiar words are found in a greater number of text samples and are more frequent. The model for word imageability ratings explained 42% of the variance in the human ratings and demonstrated that more concrete words contain attributes related to artifacts, animals, and plants, are related to two LSA dimensions, are more frequent, and are shorter.

01 01 JB code sibil.47.08ch6 06 10.1075/sibil.47.08ch6 157 184 28 Chapter 8 01 04 Chapter 6. Modelling L2 vocabulary learning Chapter 6. Modelling L2 vocabulary learning 1 A01 01 JB code 118191633 Roderick Edwards Edwards, Roderick Roderick Edwards University of Victoria, Victoria, British Columbia, Canada cand Concordia University, Montreal, Quebec, Canada. 07 https://benjamins.com/catalog/persons/118191633 2 A01 01 JB code 339191634 Laura Collins Collins, Laura Laura Collins University of Victoria, Victoria, British Columbia, Canada cand Concordia University, Montreal, Quebec, Canada. 07 https://benjamins.com/catalog/persons/339191634 01 eng 03 00

In this paper we propose a frequency-based model of vocabulary acquisition and test it on texts written by second language (L2) writers of English. One goal of the paper is to address an issue that has arisen in previous work attempting to verify Laufer and Nation’s (1995) proposal for using lexical frequency profiling tools with L2 texts to estimate the underlying vocabulary size of the L2 writers. That issue is the application of Zipf’s law (1935, 1949) directly to student texts (see Meara, 2005; Edwards & Collins, 2011), which assumes that words are learned in the order of their frequency in the language at large. As this is clearly not the case, a more valid model of vocabulary learning needs to account for the presence of less common words at different points of the acquisition process. Our model supposes that learning consists of a sequence of exposures to words, seen in proportion to their frequency in the language as a whole, and that some number of exposures are required for a word to be learned (a model parameter). This allows calculation of the probabilities that a given word (whether common or uncommon) is learned after a given number of exposures in this sequence. Furthermore, it allows calculation of the likelihood that a word is used once it has been learned, based on the word’s rank in the learner’s interlanguage (we also considered the possibility of basing this step on the word’s rank in the L2 as a whole), from which we can predict frequency distributions for learner texts. For a given 1K word count in texts, the model predicts a smaller underlying productive vocabulary than predicted by the naïve application of Zipf’s law. We then fit the parameters of the model to texts written by 90 francophone ESL learners at different points of a five-month intensive program. The best fit was obtained with a ‘number of exposures’ parameter value of 3. The model reproduces the steeper-than-Zipf tail of the frequency distribution of words observed in texts.

01 01 JB code sibil.47.09ch7 06 10.1075/sibil.47.09ch7 185 218 34 Chapter 9 01 04 Chapter 7. Vocabulary acquisition and the learning curve Chapter 7. Vocabulary acquisition and the learning curve 1 A01 01 JB code 635191635 Michael Daller Daller, Michael Michael Daller 07 https://benjamins.com/catalog/persons/635191635 2 A01 01 JB code 679191636 John Turlik Turlik, John John Turlik 07 https://benjamins.com/catalog/persons/679191636 3 A01 01 JB code 821191637 Ian Weir Weir, Ian Ian Weir 07 https://benjamins.com/catalog/persons/821191637 01 eng 03 00

Many studies in a variety of educational contexts show that learning curves are non-linear (e.g. Freedman, 1987 for the development of story telling skills in the first language, DeKeyser, 1997 for the acquisition of morphosyntactic rules of an artificial second language or Brooks and Meltzoff, 2007 for the development of vocabulary in two-year-old infants), but there is no agreement on the best non-linear model which may vary between different contexts. Although there are strong arguments, both on empirical and on theoretical grounds, that a power curve is appropriate in most educational settings (Newell & Rosenbloom, 1981; Ninio 2007) other models have also been proposed (Van de gaer et al., 2009; Verhoeven & Van Leeuwe, 2009). However, little is known about the long-term patterns of vocabulary learning in a foreign language. In the present study we analyse the vocabulary used in 294 essays by 42 students written at regular intervals over a period of two years. We use several measures that focus on vocabulary richness as well as ratings from trained IELTS teachers. Our analysis is supported with structural equation modelling, where a latent learning curve, based on the power law, can be identified. The present study is relevant for the discussion on methodological approaches in the measurement of vocabulary knowledge but also has pedagogical implications, as it allows teachers to identify when a certain plateau has been reached and when further vocabulary learning is only effective with additional pedagogical intervention.

01 01 JB code sibil.47.10ind 06 10.1075/sibil.47.10ind 219 220 2 Miscellaneous 10 01 04 Index Index 01 eng
01 JB code JBENJAMINS John Benjamins Publishing Company 01 01 JB code JB John Benjamins Publishing Company 01 https://benjamins.com 02 https://benjamins.com/catalog/sibil.47 Amsterdam NL 00 John Benjamins Publishing Company Marketing Department / Karin Plijnaar, Pieter Lamers onix@benjamins.nl 04 01 00 20130814 C 2013 John Benjamins D 2013 John Benjamins 02 WORLD 13 15 9789027241887 WORLD 09 01 JB 3 John Benjamins e-Platform 03 https://jbe-platform.com 29 https://jbe-platform.com/content/books/9789027271679 21 01 00 Unqualified price 02 90.00 EUR 01 00 Unqualified price 02 76.00 GBP GB 01 00 Unqualified price 02 135.00 USD
252011371 03 01 01 JB code JB John Benjamins Publishing Company 01 JB code SiBil 47 Hb 15 9789027241887 06 10.1075/sibil.47 13 2013019635 00 BB 08 585 gr 10 01 JB code SiBil 02 0928-1533 02 47.00 01 02 Studies in Bilingualism Studies in Bilingualism 01 01 Vocabulary Knowledge Human ratings and automated measures Vocabulary Knowledge: Human ratings and automated measures 1 B01 01 JB code 509185040 Scott Jarvis Jarvis, Scott Scott Jarvis Ohio University 07 https://benjamins.com/catalog/persons/509185040 2 B01 01 JB code 116185041 Michael Daller Daller, Michael Michael Daller Swansea University, Wales, UK 07 https://benjamins.com/catalog/persons/116185041 01 eng 11 228 03 03 viii 03 00 220 03 01 23 418.0071 03 2013 P53.9 04 Vocabulary--Ability testing. 04 Language and languages--Ability testing. 10 LAN009000 12 CFDM 24 JB code LIN.COMPUT Computational & corpus linguistics 24 JB code LIN.LA Language acquisition 24 JB code LIN.BIL Multilingualism 24 JB code LIN.PSYLIN Psycholinguistics 01 06 02 00 A collection of the latest advances, developments, and innovations regarding the modeling and measurement of learners’ vocabulary growth curves, current levels of vocabulary knowledge and lexical proficiency, and the patterns of lexical diversity found in their language production. 03 00 Language researchers and practitioners often adopt tools and techniques without testing whether they really work as they should. This is understandable because most scholars do not have the time or expertise to properly evaluate the usefulness of all instruments, measures, and methods they need. It is therefore critical to have problem solvers in the field who gain the necessary expertise and take the time to scrutinize existing methods, identify problems, and offer new solutions. This volume represents the work of scholars who have done this; it is a collection of the latest advances, developments, and innovations regarding the modeling and measurement of learners’ vocabulary growth curves, current levels of vocabulary knowledge and lexical proficiency, and the patterns of lexical diversity found in their language production. Several of the contributors also address the complex but important relationship between automated indices and human judgments of learners’ lexical patterns and abilities. 01 00 03 01 01 D503 https://benjamins.com/covers/475/sibil.47.png 01 01 D502 https://benjamins.com/covers/475_jpg/9789027241887.jpg 01 01 D504 https://benjamins.com/covers/475_tif/9789027241887.tif 01 01 D503 https://benjamins.com/covers/1200_front/sibil.47.hb.png 01 01 D503 https://benjamins.com/covers/125/sibil.47.png 02 00 03 01 01 D503 https://benjamins.com/covers/1200_back/sibil.47.hb.png 03 00 03 01 01 D503 https://benjamins.com/covers/3d_web/sibil.47.hb.png 01 01 JB code sibil.47.01aut 06 10.1075/sibil.47.01aut vii viii 2 Miscellaneous 1 01 04 Bio data of authors Bio data of authors 01 eng 01 01 JB code sibil.47.02int 06 10.1075/sibil.47.02int 1 12 12 Article 2 01 04 Introduction Introduction 1 A01 01 JB code 430191620 Scott Jarvis Jarvis, Scott Scott Jarvis 07 https://benjamins.com/catalog/persons/430191620 2 A01 01 JB code 682191621 Michael Daller Daller, Michael Michael Daller 07 https://benjamins.com/catalog/persons/682191621 01 eng 01 01 JB code sibil.47.03ch1 06 10.1075/sibil.47.03ch1 13 44 32 Chapter 3 01 04 Chapter 1. Defining and measuring lexical diversity Chapter 1. Defining and measuring lexical diversity 1 A01 01 JB code 93191622 Scott Jarvis Jarvis, Scott Scott Jarvis 07 https://benjamins.com/catalog/persons/93191622 01 eng 03 00

Most existing measures of lexical diversity are either direct or indirect measures of the proportion of repeated words in a language sample, and they tend to be validated in accordance with how well they avoid sample-size effects and/or how strongly they correlate with measures of knowledge and proficiency. The present paper argues that such measures suffer from the lack of construct validity in two ways: (a) They are not grounded in an adequate or clearly articulated theoretical account of the nature of the construct of lexical diversity, and (b) they are not validated in relation to how well they measure lexical diversity itself, but rather in relation to how well they do or do not correlate with other constructs. The present paper proposes solutions to both of these problems by defining lexical diversity as a perception-based phenomenon with six measurable properties, and by calibrating the six objective properties against human judgments of lexical diversity. The purpose of the empirical portion of the paper is to determine how well a statistical model constructed on the basis of the proposed six objective properties is able to account for nine human raters’ judgments of the lexical diversity found in 50 narratives written by adolescent learners and native speakers of English. The results support the proposed six-dimensional construct of lexical diversity, but also suggest the need for further refinements to how the six properties are measured.

01 01 JB code sibil.47.04ch2 06 10.1075/sibil.47.04ch2 45 78 34 Chapter 4 01 04 Chapter 2. From intrinsic to extrinsic issues of lexical diversity assessment Chapter 2. From intrinsic to extrinsic issues of lexical diversity assessment 01 04 An ecological validation study An ecological validation study 1 A01 01 JB code 313191623 Philip McCarthy McCarthy, Philip Philip McCarthy Decooda International and Ohio University 07 https://benjamins.com/catalog/persons/313191623 2 A01 01 JB code 721191624 Scott Jarvis Jarvis, Scott Scott Jarvis Decooda International and Ohio University 07 https://benjamins.com/catalog/persons/721191624 01 eng 03 00

Issues of lexical diversity assessment have only been addressed with consideration of the approach, rather than the corpus. Of necessity, intrinsic issues of lexical diversity related to the approach needed to be addressed first; however, given that they now have received due attention in recent research, it is time to turn our attention to extrinsic issues of lexical diversity, which is the assessment of how variations in texts and corpora affect the results of the approach. The focus of intrinsic issues has been on the algorithms and approaches used to produce values of lexical diversity on laboratory-like data sets. With extrinsic issues of word count, the focus moves to more naturalistic data sets with texts that demonstrate ranges of inconsistencies in terms of size, quality, and length. For these data, indices of lexical diversity are required to demonstrate ecological validity. The degree to which an index of lexical diversity exhibits ecological validity is of considerable importance to the field of second language learning because naturalistic corpora vary considerably in size, and texts within the corpora vary considerably in terms of word count. In other words, ecological validity is a necessary element of the construct validity of lexical diversity. In this study, we assess the three primary indices of lexical diversity (MTLD, HD-D, and Maas) using a corpus of naturalistic data in order to evaluate extrinsic issues of lexical diversity assessment by way of ecological validation. Our results show that the index of MTLD appears strongest and the index of Maas appears the least strong. Our conclusion, while encouraging broader research, is that the Maas index be abandoned as a lexical diversity index because of its over-sensitivity to word count. By contrast, MTLD appears to be resilient to a wide range of extrinsic factors and, consequently, is recommended for future lexical diversity studies.

01 01 JB code sibil.47.05ch3 06 10.1075/sibil.47.05ch3 79 104 26 Chapter 5 01 04 Chapter 3. Measuring lexical diversity among L2 learners of French Chapter 3. Measuring lexical diversity among L2 learners of French 01 04 An exploration of the validity of D, MTLD cand HD-D as measures of language ability An exploration of the validity of D, MTLD 
and HD-D as measures of language ability 1 A01 01 JB code 897191625 Jeanine Treffers-Daller Treffers-Daller, Jeanine Jeanine Treffers-Daller University of Reading 07 https://benjamins.com/catalog/persons/897191625 01 eng 03 00

In this study two new measures of lexical diversity are tested for the first time on French. The usefulness of these measures, MTLD (McCarthy and Jarvis (2010 and this volume) and HD-D (McCarthy and Jarvis 2007), in predicting different aspects of language proficiency is assessed and compared with D (Malvern and Richards 1997; Malvern, Richards, Chipere and Durán 2004) and Maas (1972) in analyses of stories told by two groups of learners (n = 41) of two different proficiency levels and one group of native speakers of French (n = 23). The importance of careful lemmatization in studies of lexical diversity which involve highly inflected languages is also demonstrated. The paper shows that the measures of lexical diversity under study are valid proxies for language ability in that they explain up to 62 percent of the variance in French C-test scores, and up to 33 percent of the variance in a measure of complexity. The paper also provides evidence that dependence on segment size continues to be a problem for the measures of lexical diversity discussed in this paper. The paper concludes that limiting the range of text lengths or even keeping text length constant is the safest option in analysing lexical diversity.

01 01 JB code sibil.47.06ch4 06 10.1075/sibil.47.06ch4 105 134 30 Chapter 6 01 04 Chapter 4. Validating lexical measures using human scores of lexical proficiency Chapter 4. Validating lexical measures using human scores of lexical proficiency 1 A01 01 JB code 352191626 Scott A. Crossley Crossley, Scott A. Scott A. Crossley Georgia State University 07 https://benjamins.com/catalog/persons/352191626 2 A01 01 JB code 301191627 Tom Salsbury Salsbury, Tom Tom Salsbury Washington State University 07 https://benjamins.com/catalog/persons/301191627 3 A01 01 JB code 619191628 Danielle S. McNamara McNamara, Danielle S. Danielle S. McNamara Arizona State University 07 https://benjamins.com/catalog/persons/619191628 01 eng 03 00

This study examines the convergent validity of a wide range of computational indices reported by Coh-Metrix that have been associated in past studies with lexical features such as basic category words, semantic co-referentiality, word frequency, and lexical diversity. This study uses human judgments of these lexical features as found in free-writing samples as operationalizations of the lexical constructs the indices are meant to measure. Statistical analyses were then conducted to examine the convergent validity of each index and to assess the predictive ability of the indices that correlate strongest with the human judgments to explain holistic scores of lexical proficiency in L1 and L2 speakers. Correlations between the automated lexical indices and the operationalized constructs demonstrated small to large effect sizes providing a degree of convergent validity for most of the automated indices examined in this study. A multiple regression predicting holistic judgments of lexical proficiency using these automated lexical indices explained 40% of the variance in a training set and 37% of the variance in a test set. The findings from the study provide a degree of confidence that the indices are measuring the constructs they were predicted to measure.

01 01 JB code sibil.47.07ch5 06 10.1075/sibil.47.07ch5 135 156 22 Chapter 7 01 04 Chapter 5. Computer simulations of MRC Psycholinguistic Database word properties Chapter 5. Computer simulations of MRC Psycholinguistic Database word properties 01 04 Concreteness, familiarity, and imageability Concreteness, familiarity, and imageability 1 A01 01 JB code 967191629 Scott A. Crossley Crossley, Scott A. Scott A. Crossley Georgia State University 07 https://benjamins.com/catalog/persons/967191629 2 A01 01 JB code 279191630 Shi Feng Feng, Shi Shi Feng University of Memphis 07 https://benjamins.com/catalog/persons/279191630 3 A01 01 JB code 595191631 Zhiqiang Cai Cai, Zhiqiang Zhiqiang Cai University of Memphis 07 https://benjamins.com/catalog/persons/595191631 4 A01 01 JB code 667191632 Danielle S. McNamara McNamara, Danielle S. Danielle S. McNamara Arizona State University 07 https://benjamins.com/catalog/persons/667191632 01 eng 03 00

This study investigates the potential for computational models informed through automated lexical indices to simulate human ratings of word concreteness, word familiarity, and word imageability. The goal of the study is to provide word information estimates for words with human ratings, thereby affording greater textual coverage and permitting a better understanding of features that underlie word properties. This study uses traditional automated word features such word length, word frequency, hypernymy, and polysemy along with novel automated word features such as word type attributes taken from WordNet, LSA dimensions, and inverse entropy weights as predictor variables. The model reported in this study for word concreteness predicted 61% of the variance in human ratings of word concreteness and demonstrated that more concrete words contain attributes related to people, animals, and food, have higher hypernymy levels, are related to two LSA dimensions, are more frequent, and are shorter. The model for word familiarity predicted 62% of the variance in the human ratings reported in the MRC database and demonstrated that more familiar words are found in a greater number of text samples and are more frequent. The model for word imageability ratings explained 42% of the variance in the human ratings and demonstrated that more concrete words contain attributes related to artifacts, animals, and plants, are related to two LSA dimensions, are more frequent, and are shorter.

01 01 JB code sibil.47.08ch6 06 10.1075/sibil.47.08ch6 157 184 28 Chapter 8 01 04 Chapter 6. Modelling L2 vocabulary learning Chapter 6. Modelling L2 vocabulary learning 1 A01 01 JB code 118191633 Roderick Edwards Edwards, Roderick Roderick Edwards University of Victoria, Victoria, British Columbia, Canada cand Concordia University, Montreal, Quebec, Canada. 07 https://benjamins.com/catalog/persons/118191633 2 A01 01 JB code 339191634 Laura Collins Collins, Laura Laura Collins University of Victoria, Victoria, British Columbia, Canada cand Concordia University, Montreal, Quebec, Canada. 07 https://benjamins.com/catalog/persons/339191634 01 eng 03 00

In this paper we propose a frequency-based model of vocabulary acquisition and test it on texts written by second language (L2) writers of English. One goal of the paper is to address an issue that has arisen in previous work attempting to verify Laufer and Nation’s (1995) proposal for using lexical frequency profiling tools with L2 texts to estimate the underlying vocabulary size of the L2 writers. That issue is the application of Zipf’s law (1935, 1949) directly to student texts (see Meara, 2005; Edwards & Collins, 2011), which assumes that words are learned in the order of their frequency in the language at large. As this is clearly not the case, a more valid model of vocabulary learning needs to account for the presence of less common words at different points of the acquisition process. Our model supposes that learning consists of a sequence of exposures to words, seen in proportion to their frequency in the language as a whole, and that some number of exposures are required for a word to be learned (a model parameter). This allows calculation of the probabilities that a given word (whether common or uncommon) is learned after a given number of exposures in this sequence. Furthermore, it allows calculation of the likelihood that a word is used once it has been learned, based on the word’s rank in the learner’s interlanguage (we also considered the possibility of basing this step on the word’s rank in the L2 as a whole), from which we can predict frequency distributions for learner texts. For a given 1K word count in texts, the model predicts a smaller underlying productive vocabulary than predicted by the naïve application of Zipf’s law. We then fit the parameters of the model to texts written by 90 francophone ESL learners at different points of a five-month intensive program. The best fit was obtained with a ‘number of exposures’ parameter value of 3. The model reproduces the steeper-than-Zipf tail of the frequency distribution of words observed in texts.

01 01 JB code sibil.47.09ch7 06 10.1075/sibil.47.09ch7 185 218 34 Chapter 9 01 04 Chapter 7. Vocabulary acquisition and the learning curve Chapter 7. Vocabulary acquisition and the learning curve 1 A01 01 JB code 635191635 Michael Daller Daller, Michael Michael Daller 07 https://benjamins.com/catalog/persons/635191635 2 A01 01 JB code 679191636 John Turlik Turlik, John John Turlik 07 https://benjamins.com/catalog/persons/679191636 3 A01 01 JB code 821191637 Ian Weir Weir, Ian Ian Weir 07 https://benjamins.com/catalog/persons/821191637 01 eng 03 00

Many studies in a variety of educational contexts show that learning curves are non-linear (e.g. Freedman, 1987 for the development of story telling skills in the first language, DeKeyser, 1997 for the acquisition of morphosyntactic rules of an artificial second language or Brooks and Meltzoff, 2007 for the development of vocabulary in two-year-old infants), but there is no agreement on the best non-linear model which may vary between different contexts. Although there are strong arguments, both on empirical and on theoretical grounds, that a power curve is appropriate in most educational settings (Newell & Rosenbloom, 1981; Ninio 2007) other models have also been proposed (Van de gaer et al., 2009; Verhoeven & Van Leeuwe, 2009). However, little is known about the long-term patterns of vocabulary learning in a foreign language. In the present study we analyse the vocabulary used in 294 essays by 42 students written at regular intervals over a period of two years. We use several measures that focus on vocabulary richness as well as ratings from trained IELTS teachers. Our analysis is supported with structural equation modelling, where a latent learning curve, based on the power law, can be identified. The present study is relevant for the discussion on methodological approaches in the measurement of vocabulary knowledge but also has pedagogical implications, as it allows teachers to identify when a certain plateau has been reached and when further vocabulary learning is only effective with additional pedagogical intervention.

01 01 JB code sibil.47.10ind 06 10.1075/sibil.47.10ind 219 220 2 Miscellaneous 10 01 04 Index Index 01 eng
01 JB code JBENJAMINS John Benjamins Publishing Company 01 01 JB code JB John Benjamins Publishing Company 01 https://benjamins.com 02 https://benjamins.com/catalog/sibil.47 Amsterdam NL 00 John Benjamins Publishing Company Marketing Department / Karin Plijnaar, Pieter Lamers onix@benjamins.nl 04 01 00 20130814 C 2013 John Benjamins D 2013 John Benjamins 02 WORLD WORLD US CA MX 09 01 JB 1 John Benjamins Publishing Company +31 20 6304747 +31 20 6739773 bookorder@benjamins.nl 01 https://benjamins.com 21 43 20 01 00 Unqualified price 02 JB 1 02 90.00 EUR 02 00 Unqualified price 02 76.00 01 Z 0 GBP GB US CA MX 01 01 JB 2 John Benjamins Publishing Company +1 800 562-5666 +1 703 661-1501 benjamins@presswarehouse.com 01 https://benjamins.com 21 43 20 01 00 Unqualified price 02 JB 1 02 135.00 USD
334014904 03 01 01 JB code JB John Benjamins Publishing Company 01 JB code SiBil 47 GE 15 9789027271679 06 10.1075/sibil.47 00 EA E133 10 01 JB code SiBil 02 JB code 0928-1533 02 47.00 01 02 Studies in Bilingualism Studies in Bilingualism 01 01 Vocabulary Knowledge Vocabulary Knowledge 1 B01 01 JB code 509185040 Scott Jarvis Jarvis, Scott Scott Jarvis Ohio University 2 B01 01 JB code 116185041 Michael Daller Daller, Michael Michael Daller Swansea University, Wales, UK 01 eng 11 228 03 03 viii 03 00 220 03 24 JB code LIN.COMPUT Computational & corpus linguistics 24 JB code LIN.LA Language acquisition 24 JB code LIN.BIL Multilingualism 24 JB code LIN.PSYLIN Psycholinguistics 10 LAN009000 12 CFDM 01 06 02 00 A collection of the latest advances, developments, and innovations regarding the modeling and measurement of learners’ vocabulary growth curves, current levels of vocabulary knowledge and lexical proficiency, and the patterns of lexical diversity found in their language production. 03 00 Language researchers and practitioners often adopt tools and techniques without testing whether they really work as they should. This is understandable because most scholars do not have the time or expertise to properly evaluate the usefulness of all instruments, measures, and methods they need. It is therefore critical to have problem solvers in the field who gain the necessary expertise and take the time to scrutinize existing methods, identify problems, and offer new solutions. This volume represents the work of scholars who have done this; it is a collection of the latest advances, developments, and innovations regarding the modeling and measurement of learners’ vocabulary growth curves, current levels of vocabulary knowledge and lexical proficiency, and the patterns of lexical diversity found in their language production. Several of the contributors also address the complex but important relationship between automated indices and human judgments of learners’ lexical patterns and abilities. 01 00 03 01 01 D503 https://benjamins.com/covers/475/sibil.47.png 01 01 D502 https://benjamins.com/covers/475_jpg/9789027241887.jpg 01 01 D504 https://benjamins.com/covers/475_tif/9789027241887.tif 01 01 D503 https://benjamins.com/covers/1200_front/sibil.47.hb.png 01 01 D503 https://benjamins.com/covers/125/sibil.47.png 02 00 03 01 01 D503 https://benjamins.com/covers/1200_back/sibil.47.hb.png 03 00 03 01 01 D503 https://benjamins.com/covers/3d_web/sibil.47.hb.png 01 01 JB code sibil.47.01aut 06 10.1075/sibil.47.01aut vii viii 2 Miscellaneous 1 01 04 Bio data of authors Bio data of authors 01 01 JB code sibil.47.02int 06 10.1075/sibil.47.02int 1 12 12 Article 2 01 04 Introduction Introduction 1 A01 01 JB code 430191620 Scott Jarvis Jarvis, Scott Scott Jarvis 2 A01 01 JB code 682191621 Michael Daller Daller, Michael Michael Daller 01 01 JB code sibil.47.03ch1 06 10.1075/sibil.47.03ch1 13 44 32 Chapter 3 01 04 Chapter 1. Defining and measuring lexical diversity Chapter 1. Defining and measuring lexical diversity 1 A01 01 JB code 93191622 Scott Jarvis Jarvis, Scott Scott Jarvis 01 01 JB code sibil.47.04ch2 06 10.1075/sibil.47.04ch2 45 78 34 Chapter 4 01 04 Chapter 2. From intrinsic to extrinsic issues of lexical diversity assessment Chapter 2. From intrinsic to extrinsic issues of lexical diversity assessment 01 04 An ecological validation study An ecological validation study 1 A01 01 JB code 313191623 Philip McCarthy McCarthy, Philip Philip McCarthy Decooda International and Ohio University 2 A01 01 JB code 721191624 Scott Jarvis Jarvis, Scott Scott Jarvis Decooda International and Ohio University 01 01 JB code sibil.47.05ch3 06 10.1075/sibil.47.05ch3 79 104 26 Chapter 5 01 04 Chapter 3. Measuring lexical diversity among L2 learners of French Chapter 3. Measuring lexical diversity among L2 learners of French 01 04 An exploration of the validity of D, MTLD cand HD-D as measures of language ability An exploration of the validity of D, MTLD 
and HD-D as measures of language ability 1 A01 01 JB code 897191625 Jeanine Treffers-Daller Treffers-Daller, Jeanine Jeanine Treffers-Daller University of Reading 01 01 JB code sibil.47.06ch4 06 10.1075/sibil.47.06ch4 105 134 30 Chapter 6 01 04 Chapter 4. Validating lexical measures using human scores of lexical proficiency Chapter 4. Validating lexical measures using human scores of lexical proficiency 1 A01 01 JB code 352191626 Scott A. Crossley Crossley, Scott A. Scott A. Crossley Georgia State University 2 A01 01 JB code 301191627 Tom Salsbury Salsbury, Tom Tom Salsbury Washington State University 3 A01 01 JB code 619191628 Danielle S. McNamara McNamara, Danielle S. Danielle S. McNamara Arizona State University 01 01 JB code sibil.47.07ch5 06 10.1075/sibil.47.07ch5 135 156 22 Chapter 7 01 04 Chapter 5. Computer simulations of MRC Psycholinguistic Database word properties Chapter 5. Computer simulations of MRC Psycholinguistic Database word properties 01 04 Concreteness, familiarity, and imageability Concreteness, familiarity, and imageability 1 A01 01 JB code 967191629 Scott A. Crossley Crossley, Scott A. Scott A. Crossley Georgia State University 2 A01 01 JB code 279191630 Shi Feng Feng, Shi Shi Feng University of Memphis 3 A01 01 JB code 595191631 Zhiqiang Cai Cai, Zhiqiang Zhiqiang Cai University of Memphis 4 A01 01 JB code 667191632 Danielle S. McNamara McNamara, Danielle S. Danielle S. McNamara Arizona State University 01 01 JB code sibil.47.08ch6 06 10.1075/sibil.47.08ch6 157 184 28 Chapter 8 01 04 Chapter 6. Modelling L2 vocabulary learning Chapter 6. Modelling L2 vocabulary learning 1 A01 01 JB code 118191633 Roderick Edwards Edwards, Roderick Roderick Edwards University of Victoria, Victoria, British Columbia, Canada cand Concordia University, Montreal, Quebec, Canada. 2 A01 01 JB code 339191634 Laura Collins Collins, Laura Laura Collins University of Victoria, Victoria, British Columbia, Canada cand Concordia University, Montreal, Quebec, Canada. 01 01 JB code sibil.47.09ch7 06 10.1075/sibil.47.09ch7 185 218 34 Chapter 9 01 04 Chapter 7. Vocabulary acquisition and the learning curve Chapter 7. Vocabulary acquisition and the learning curve 1 A01 01 JB code 635191635 Michael Daller Daller, Michael Michael Daller 2 A01 01 JB code 679191636 John Turlik Turlik, John John Turlik 3 A01 01 JB code 821191637 Ian Weir Weir, Ian Ian Weir 01 01 JB code sibil.47.10ind 06 10.1075/sibil.47.10ind 219 220 2 Miscellaneous 10 01 04 Index Index 01 JB code JBENJAMINS John Benjamins Publishing Company 01 01 JB code JB John Benjamins Publishing Company 01 https://benjamins.com Amsterdam NL 00 John Benjamins Publishing Company Marketing Department / Karin Plijnaar, Pieter Lamers onix@benjamins.nl 04 01 00 20130814 C 2013 John Benjamins D 2013 John Benjamins 02 WORLD 13 15 9789027241887 WORLD 03 01 JB 17 Google 03 https://play.google.com/store/books 21 01 00 Unqualified price 00 90.00 EUR 01 00 Unqualified price 00 76.00 GBP 01 00 Unqualified price 00 135.00 USD