Work

Language Muse: Automated Linguistic Activity Generation for English Language Learners

Year: 2016

Type: conference-paper

Abstract: Current education standards in the U.S. require school students to read and understand complex texts from different subject areas (e.g., social studies).However, such texts usually contain figurative language, complex phrases and sentences, as well as unfamiliar discourse relations.This may present an obstacle to students whose native language is not English -a growing sub-population in the US. 1 One way to help such students is to create classroom activities centered around linguistic elements found in subject area texts (DelliCarpini, 2008).We present a web-based tool that uses NLP algorithms to automatically generate customizable linguistic activities that are grounded in language learning research.

Source: Annual Meeting of the Association for Computational Linguistics: System Demonstrations (ACL Demo)

DOI: 10.18653/v1/p16-4014

Authors: Nitin Madnani, Jill Burstein, John Sabatini, Kietha Biggers, Slava Andreyev

Institution: Educational Testing Service

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: hybrid

OpenAlex ID: W2513201941