Automatic analysis of syntactic complexity in second language writing

Lu, Xiaofei

doi:10.1075/ijcl.15.4.02lu

Cited by 722 publications

(555 citation statements)

References 29 publications

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“…Most work in automated scoring and learner language analysis has focused on detecting grammar and usage errors (Leacock et al, 2014;Gamon, 2010;Chodorow et al, 2007;Lu, 2010). This is done either by means of handcrafted rules or with statistical classifiers using a variety of information.…”

Section: Related Workmentioning

confidence: 99%

Automated Measures of Specific Vocabulary Knowledge from Constructed Responses ('Use These Words to Write a Sentence Based on this Picture')

Somasundaran

Chodorow

2014

Proceedings of the Ninth Workshop on Innovative Use of NLP for Building Educational Applications

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We describe a system for automatically scoring a vocabulary item type that asks test-takers to use two specific words in writing a sentence based on a picture. The system consists of a rule-based component and a machine learned statistical model which uses a variety of construct-relevant features. Specifically, in constructing the statistical model, we investigate if grammar, usage, and mechanics features developed for scoring essays can be applied to short answers, as in our task. We also explore new features reflecting the quality of the collocations in the response, as well as features measuring the consistency of the response to the picture. System accuracy in scoring is 15 percentage points greater than the majority class baseline and 10 percentage points less than human performance.

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Section: Related Workmentioning

confidence: 99%

Automated Measures of Specific Vocabulary Knowledge from Constructed Responses ('Use These Words to Write a Sentence Based on this Picture')

Somasundaran

Chodorow

2014

Proceedings of the Ninth Workshop on Innovative Use of NLP for Building Educational Applications

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“…We used the L2 Syntactic Complexity Analyzer (Lu, 2010 processing and learnability (Salsbury et al, 2011). We used five psycholinguisic features: Familiarity, Concreteness, Imageability, Age of acquisition, and the SUBTL , which is a measure of the frequency with which a word is used in daily life (Kuperman et al, 2012;Brysbaert and New, 2009a;Salsbury et al, 2011).…”

Section: Parts Of Speech (14)mentioning

confidence: 99%

Detecting Dementia through Retrospective Analysis of Routine Blog Posts by Bloggers with Dementia

Masrani¹,

Murray²,

Field³

et al. 2017

BioNLP 2017

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We investigate if writers with dementia can be automatically distinguished from those without by analyzing linguistic markers in written text, in the form of blog posts. We have built a corpus of several thousand blog posts, some by people with dementia and others by people with loved ones with dementia. We use this dataset to train and test several machine learning methods, and achieve prediction performance at a level far above the baseline.

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“…However, we argue that a corpus-based study practically involves a quantitative as well as a qualitative approach (Biber et al 1998). Lu (2010) posits that to facilitate application of a large set of syntactic complexity measures of interest to large-scale corpus data, it is clearly necessary to develop computational tools that can automate analysis of syntactic complexity in second language production using those measures. In other words, statistical data obtained through quantitative technique help provide ideas and patterns based on evidential bases.…”

Section: Methodsmentioning

confidence: 99%