Papers Complex Word Identification

Multilingual Complex Word Identification: Convolutional Neural Networks with Morphological and Linguistic Features

The paper is about our experiments with Complex Word Identification system using deep learning approach with word embeddings and engineered features.

Comparative judgments are more consistent than binary classification for labelling word complexity

Lexical simplification systems replace complex words with simple ones based on a model of which words are complex in context. We explore how users can help train complex word identification models through labelling more …

Complex Word Identification as a Sequence Labelling Task

Complex Word Identification (CWI) is concerned with detection of words in need of simplification and is a crucial first step in a simplification pipeline. It has been shown that reliable CWI systems considerably improve …

Strong Baselines for Complex Word Identification across Multiple Languages

Complex Word Identification (CWI) is the task of identifying which words or phrases in a sentence are difficult to understand by a target audience. The latest CWI Shared Task released data for two settings: monolingual (…

The Interface Between Readability and Automatic Text Simplification

Personalizing Lexical Simplification

A lexical simplification (LS) system aims to substitute complex words with simple words in a text, while preserving its meaning and grammaticality. Despite individual users{'} differences in vocabulary knowledge, current…

Personalized Text Retrieval for Learners of Chinese as a Foreign Language

This paper describes a personalized text retrieval algorithm that helps language learners select the most suitable reading material in terms of vocabulary complexity. The user first rates their knowledge of a small set o…

Simplification Using Paraphrases and Context-Based Lexical Substitution

Lexical simplification involves identifying complex words or phrases that need to be simplified, and recommending simpler meaning-preserving substitutes that can be more easily understood. We propose a complex word ident…

NT2Lex: A CEFR-Graded Lexical Resource for Dutch as a Foreign Language Linked to Open Dutch WordNet

In this paper, we introduce NT2Lex, a novel lexical resource for Dutch as a foreign language (NT2) which includes frequency distributions of 17,743 words and expressions attested in expert-written textbook texts and read…

LaSTUS/TALN at Complex Word Identification (CWI) 2018 Shared Task

This paper presents the participation of the LaSTUS/TALN team in the Complex Word Identification (CWI) Shared Task 2018 in the English monolingual track . The purpose of the task was to determine if a word in a given sen…

Cross-lingual complex word identification with multitask learning

We approach the 2018 Shared Task on Complex Word Identification by leveraging a cross-lingual multitask learning approach. Our method is highly language agnostic, as evidenced by the ability of our system to generalize a…

CAMB at CWI Shared Task 2018: Complex Word Identification with Ensemble-Based Voting

This paper presents the winning systems we submitted to the Complex Word Identification Shared Task 2018. We describe our best performing systems{'} implementations and discuss our key findings from this research. Our be…

Complex Word Identification Based on Frequency in a Learner Corpus

We introduce the TMU systems for the Complex Word Identification (CWI) Shared Task 2018. TMU systems use random forest classifiers and regressors whose features are the number of characters, the number of words, and the …

The Whole is Greater than the Sum of its Parts: Towards the Effectiveness of Voting Ensemble Classifiers for Complex Word Identification

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 class…

SB@GU at the Complex Word Identification 2018 Shared Task

In this paper, we describe our experiments for the Shared Task on Complex Word Identification (CWI) 2018 (Yimam et al., 2018), hosted by the 13th Workshop on Innovative Use of NLP for Building Educational Applications (B…

Complex Word Identification: Convolutional Neural Network vs. Feature Engineering

We describe the systems of NLP-CIC team that participated in the Complex Word Identification (CWI) 2018 shared task. The shared task aimed to benchmark approaches for identifying complex words in English and other langua…

Deep Learning Architecture for Complex Word Identification

We describe a system for the CWI-task that includes information on 5 aspects of the (complex) lexical item, namely distributional information of the item itself, morphological structure, psychological measures, corpus-co…

NILC at CWI 2018: Exploring Feature Engineering and Feature Learning

This paper describes the results of NILC team at CWI 2018. We developed solutions following three approaches: (i) a feature engineering method using lexical, n-gram and psycholinguistic features, (ii) a shallow neural ne…

Complex Word Identification Using Character n-grams

This paper investigates the use of character n-gram frequencies for identifying complex words in English, German and Spanish texts. The approach is based on the assumption that complex words are likely to contain differe…

A Report on the Complex Word Identification Shared Task 2018

We report the findings of the second Complex Word Identification (CWI) shared task organized as part of the BEA workshop co-located with NAACL-HLT'2018. The second CWI shared task featured multilingual and multi-genre da…