Abstract

In this paper, we present an effective system using voting ensemble classifiers to detect contextually complex words for non-native English speakers. To make the final decision, we channel a set of eight calibrated classifiers based on lexical, size and vocabulary features and train our model with annotated datasets collected from a mixture of native and non-native speakers. Thereafter, we test our system on three datasets namely News, WikiNews, and Wikipedia and report competitive results with an F1-Score ranging between 0.777 to 0.855 for each of the datasets. Our system outperforms multiple other models and falls within 0.042 to 0.026 percent of the best-performing model’s score in the shared task.

Highlights

  • Complex Word Identification (CWI) is an essential sub-task for Lexical Simplification

  • It is geared for target population like non-native speakers, second-language learners, young learners, and people with language disabilities, with the aim of allowing them to comprehend the presented text completely

  • Unlike the SemEval 2016 shared task, the target words here could have more than one word, and the context could stretch over multiple sentences

Read more

Summary

Introduction

Complex Word Identification (CWI) is an essential sub-task for Lexical Simplification. Lexical Simplification involves substituting a complicated word in the text with a more straightforward synonym. It is geared for target population like non-native speakers, second-language learners, young learners, and people with language disabilities (like Aphasia and Alexia), with the aim of allowing them to comprehend the presented text completely. The goal of the shared task is as follows: Given a target word (or phrase) and its context, we are to computationally determine if the target word is complex or not. Unlike the SemEval 2016 shared task, the target words here could have more than one word (e.g., teenage girl), and the context could stretch over multiple sentences

Methods
Results
Discussion
Conclusion
Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.