1 We report experience in applying techniques for natural language processing to algorithmically generating test items for both reading and listening cloze items. We propose a word sense disambiguationbased method for locating sentences in which designated words carry specific senses, and apply a collocation-based method for selecting distractors that are necessary for multiple-choice cloze ite...

This paper describes and evaluates DQGen, which automatically generates multiple choice cloze questions to test a child’s comprehension while reading a given text. Unlike previous methods, it generates different types of distracters designed to diagnose different types of comprehension failure, and tests comprehension not only of an individual sentence but of the context that precedes it. We ev...

The objectives of this study are to analyze about the different and similar multiple choice cloze test in reading comprehension, perception toward among students that influence comprehension. This used descriptive qualitative method. In collecting data, used, Observation, Interview, Questionnaire, Documentaton. data were taken by choice. was conducted there 21 informants which 7 boys 13 girls S...

This article reports on case-study research which explored English as a second language (ESL) learners’ perceptions of the role played by the literary cloze activity in the development of their language and literary awareness. The subjects, four students from a variety of language, culturaland literarybackgrounds, attended a semester-long university ESL subject which adopted a language-based ap...

Can automatically generated questions scaffold reading comprehension? We automated three kinds of multiple-choice questions in children’s assisted reading: 1. Whquestions: ask a generically worded What/Where/When question. 2. Sentence prediction: ask which of three sentences belongs next. 3. Cloze: ask which of four words best fills in a blank in the next sentence. A within-subject experiment i...

In this paper, we propose a novel configurable framework to automatically generate distractive choices for open-domain cloze-style multiple-choice questions. The incorporates general-purpose knowledge base effectively create small distractor candidate set, and feature-rich learning-to-rank model select distractors that are both plausible reliable. Experimental results on new dataset across four...

Computer “reading” of natural languages is a growing research topic with numerous applications, ranging from areas of artificial intelligence to linguistics. A successful “reading” of natural languages can be interpreted in two ways: 1) extracting facts out of a passage and 2) capturing “meaning” of a text. We focus on the latter interpretation, defining “meaning” as is done in humans, namely, ...

Computer generation of cloze tasks still falls short of full automation; most current systems are used by teachers as authoring aids. Improved methods to estimate cloze quality are needed for full automation. We investigated lexical reading difficulty as a novel automatic estimator of cloze quality, to which cooccurrence frequency of words was compared as an alternate estimator. Rather than rel...