Automatic Distractor Generation for Domain Specific Texts

Aldabe, Itziar; Maritxalar, Montse

doi:10.1007/978-3-642-14770-8_5

Cited by 32 publications

(25 citation statements)

References 9 publications

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“…They also used natural language corpora and ontology such as WordNet (Miller, 1995;Mitkov et al, 2006). Aldabe and Maritxalar and Aldabe et al developed systems to generate MCQ in Basque language (Aldabe, de Lacalle, Maritxalar, Martinez, Uria, 2006;Aldabe & Maritxalar, 2010). Chen et al proposed a technique for semi-automatic generation of grammar based test items by using NLP techniques.…”

Section: Related Workmentioning

confidence: 99%

Factual open cloze question generation for assessment of learner’s knowledge

Das

Majumder

2017

Int J Educ Technol High Educ

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Factual objective type questions are effectively used in active learning, information and communication technology based education and intelligent tutoring system for the assessment of learner's content knowledge. In this paper, we have presented an automatic factual open cloze question generation system which can generate fill-in-the-blank questions without alternatives. In order to generate the questions, the system first extracts a set of informative sentences from the given input corpus. The sentences are considered as informative based on part-of-speech tags and certain rules. After the identification of the informative sentences the questions are generated by omitting the answer-keys which are selected by identifying domain specific words in the sentences. The unbound option set of an open cloze question often confuses the examinees. However, open cloze questions require more productive knowledge from learners than cloze questions. Finally, we have also suggested answer hints for the examinees to reduce the number of possible answers that make assessment easier.

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Section: Related Workmentioning

confidence: 99%

Factual open cloze question generation for assessment of learner’s knowledge

Das

Majumder

2017

Int J Educ Technol High Educ

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“…A stem is a good question or problem to be solved 15 . To identify a stem and generate a GFQ, an informative sentence is selected from a given document.…”

Section: Gap-fill Questionsmentioning

confidence: 99%

Automatic Question Generation Approaches and Evaluation Techniques

Divate¹,

Salgaonkar²

2017

Current Science

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The objective of this article is to review several automatic question generation systems and find why automatic question generation is still an attraction for researchers. The focus is mainly on the task of question generation, analysis of the approaches and evaluation of various methods of automatic question generation. Pointers for further research are included.Keywords: Automatic question generation, evaluation techniques, quality enhancers, ranking, sentence simplification.A study reveals that an average student asks over 26 questions per hour in one-on-one human tutoring sessions; in contrast, the student poses 120 questions per hour in a learning environment that forces her to ask questions in order to access any information 1 . Conversely, students learn more deeply if prompted by questions 2 . Conventionally, questions are constructed and assessed by tutors. It has been a trend for several decades that automatic question generation (AQG) system generates questions from the corpora using natural language processing.AQG systems were first developed in the 1976 (ref.3). They have been created for English language and vocabulary, medicine, education and using multimedia. The sequence of developments is as follows: learning words [4][5][6] , English 7 , grammar testing 8 , medicine 9 , academic writing 10 , literature review 11 , education 12 (henceforth, Heilman and Smith AQG is abbreviated as HSAQG), multimedia 13 , and finally a recent major development, on-line learning 14 . This article presents a review of more than 50 contributions in the domain of AQG. Types of questionsIn classroom practice, a tutor evaluates the comprehension of a learner by asking gap-fill type questions (GFQs), multiple choice questions (MCQs), factoid-based questions (FBQs) and deep learning-type questions (DLQs). Gap-fill questionsA stem is a good question or problem to be solved 15 . To identify a stem and generate a GFQ, an informative sentence is selected from a given document. The selection of information involves identification of semantic features in the entire document.Next, a key phrase or answer phrase (assume it is a noun phrase) is selected; term frequency plays an important role. A distractor (not expected to occur in the question) is a choice given to a learner. A good distractor could be a synonym of the key phrase or an important term in the domain of the key phrase. Distractors in Revup, an AQG, are selected from word2vec, a vector of words 16 . Text summarization features like length of a sentence, number of common tokens, number of noun and pronouns, and position of a sentence are generally considered 17 .

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“…Several similarity measures have been employed for selecting answer distractors (Mitkov et al, 2009), including measures derived from WordNet (Mitkov and Ha, 2003), thesauri (Sumita et al, 2005) and distributional context (Pino et al, 2008;Aldabe and Maritxalar, 2010). Domainspecific ontologies (Papasalouros et al, 2008), phonetic or morphological similarity (Pino and Esknazi, 2009;Correia et al, 2010), probability scores for the question context (Mostow and Jang, 2012) and context-sensitive lexical inference (Zesch and Melamud, 2014) have also been used.…”

Section: Related Workmentioning

confidence: 99%

Proceedings of the 3rd Workshop on Noisy User-generated Text

Derczynski¹,

Xu²,

Ritter³

et al. 2017

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ii Introduction The W-NUT 2017 workshop focuses on a core set of natural language processing tasks on top of noisy user-generated text, such as that found on social media, web forums and online reviews. Recent years have seen a significant increase of interest in these areas. The internet has democratized content creation leading to an explosion of informal user-generated text, publicly available in electronic format, motivating the need for NLP on noisy text to enable new data analytics applications. The workshop is an opportunity to bring together researchers interested in noisy text with different backgrounds and encourage crossover. The workshop this year features a shared task on Emerging and Rare entity recognition.The workshop received 27 main track submissions, 17 of which were accepted, in addition to 6 system description papers for the shared task and a task overview paper. There are 3 invited speakers, Bill Dolan, Dirk Hovy and Miles Osborne with each of their talks covering a different aspect of NLP for user-generated text. We would like to thank the Program Committee members who reviewed the papers this year. We would also like to thank the workshop participants. AbstractThis work presents a fine-grained, textchunking algorithm designed for the task of multiword expressions (MWEs) segmentation. As a lexical class, MWEs include a wide variety of idioms, whose automatic identification are a necessity for the handling of colloquial language. This algorithm's core novelty is its use of non-word tokens, i.e., boundaries, in a bottom-up strategy. Leveraging boundaries refines token-level information, forging highlevel performance from relatively basic data. The generality of this model's feature space allows for its application across languages and domains. Experiments spanning 19 different languages exhibit a broadly-applicable, stateof-the-art model. Evaluation against recent shared-task data places text partitioning as the overall, best performing MWE segmentation algorithm, covering all MWE classes and multiple English domains (including usergenerated text). This performance, coupled with a non-combinatorial, fast-running design, produces an ideal combination for implementations at scale, which are facilitated through the release of open-source software.

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Automatic Distractor Generation for Domain Specific Texts

Cited by 32 publications

References 9 publications

Factual open cloze question generation for assessment of learner’s knowledge

Factual open cloze question generation for assessment of learner’s knowledge

Automatic Question Generation Approaches and Evaluation Techniques

Proceedings of the 3rd Workshop on Noisy User-generated Text

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