e-space
Manchester Metropolitan University's Research Repository

    Predicting lexical complexity in English texts: the Complex 2.0 dataset

    Shardlow, Matthew ORCID logoORCID: https://orcid.org/0000-0003-1129-2750, Evans, Richard and Zampieri, Marcos (2022) Predicting lexical complexity in English texts: the Complex 2.0 dataset. Language Resources and Evaluation. ISSN 0010-4817

    [img]
    Preview
    Published Version
    Available under License Creative Commons Attribution.

    Download (1MB) | Preview

    Abstract

    Identifying words which may cause difficulty for a reader is an essential step in most lexical text simplification systems prior to lexical substitution and can also be used for assessing the readability of a text. This task is commonly referred to as complex word identification (CWI) and is often modelled as a supervised classification problem. For training such systems, annotated datasets in which words and sometimes multi-word expressions are labelled regarding complexity are required. In this paper we analyze previous work carried out in this task and investigate the properties of CWI datasets for English. We develop a protocol for the annotation of lexical complexity and use this to annotate a new dataset, CompLex 2.0. We present experiments using both new and old datasets to investigate the nature of lexical complexity. We found that a Likert-scale annotation protocol provides an objective setting that is superior for identifying the complexity of words compared to a binary annotation protocol. We release a new dataset using our new protocol to promote the task of Lexical Complexity Prediction.

    Impact and Reach

    Statistics

    Activity Overview
    6 month trend
    25Downloads
    6 month trend
    23Hits

    Additional statistics for this dataset are available via IRStats2.

    Altmetric

    Actions (login required)

    View Item View Item