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Found 15 papers, 2 papers with code
Using Linguistic Features to Predict the Response Process Complexity Associated with Answering Clinical MCQs
This study examines the relationship between the linguistic characteristics of a test item and the complexity of the response process required to answer it correctly.
Automated Prediction of Examinee Proficiency from Short-Answer Questions
This paper brings together approaches from the fields of NLP and psychometric measurement to address the problem of predicting examinee proficiency from responses to short-answer questions (SAQs).
Verbal Multiword Expressions for Identification of Metaphor
Metaphor is a linguistic device in which a concept is expressed by mentioning another.
Classifying Referential and Non-referential It Using Gaze
When processing a text, humans and machines must disambiguate between different uses of the pronoun it, including non-referential, nominal anaphoric or clause anaphoric ones.
A First Dataset for Film Age Appropriateness Investigation
Film age appropriateness classification is an important problem with a significant societal impact that has so far been out of the interest of Natural Language Processing and Machine Learning researchers.
Predicting Item Survival for Multiple Choice Questions in a High-Stakes Medical Exam
One of the most resource-intensive problems in the educational testing industry relates to ensuring that newly-developed exam questions can adequately distinguish between students of high and low ability.
Automatic Question Answering for Medical MCQs: Can It go Further than Information Retrieval?
We present a novel approach to automatic question answering that does not depend on the performance of an information retrieval (IR) system and does not require that the training data come from the same source as the questions.
A Survey of the Perceived Text Adaptation Needs of Adults with Autism
NLP approaches to automatic text adaptation often rely on user-need guidelines which are generic and do not account for the differences between various types of target groups.
Predicting the Difficulty of Multiple Choice Questions in a High-stakes Medical Exam
To accomplish this, we extract a large number of linguistic features and embedding types, as well as features quantifying the difficulty of the items for an automatic question-answering system.
Cross-lingual Transfer Learning and Multitask Learning for Capturing Multiword Expressions
Recent developments in deep learning have prompted a surge of interest in the application of multitask and transfer learning to NLP problems.
Bridging the Gap: Attending to Discontinuity in Identification of Multiword Expressions
We introduce a new method to tag Multiword Expressions (MWEs) using a linguistically interpretable language-independent deep learning architecture.
Automatic Distractor Suggestion for Multiple-Choice Tests Using Concept Embeddings and Information Retrieval
We frame the evaluation as a prediction task where we aim to {``}predict{''} the human-produced distractors used in large sets of medical questions, i. e. if a distractor generated by our system is good enough it is likely to feature among the list of distractors produced by the human item-writers.
Using Gaze Data to Predict Multiword Expressions
In recent years gaze data has been increasingly used to improve and evaluate NLP models due to the fact that it carries information about the cognitive processing of linguistic phenomena.
