L ang R esearch L ab NC at S em E val-2021 Task 1: Linguistic Feature Based Modelling for Lexical Complexity (2021)

Abstract: The present work aims at assigning a complexity score between 0 and 1 to a target word or phrase in a given sentence. For each Single Word Target, a Random Forest Regressor is trained on a feature set consisting of lexical, semantic, and syntactic information about the target. For each Multiword Target, a set of individual word features is taken along with single word complexities in the feature space. The system yielded the Pearson correlation of 0.7402 and 0.8244 on the test set for the Single and Multiword Targets, respectively.

## introduction presence of complex words can lead to poor comprehension of a text. identification of such complex words in a given text is a core component in the task of automatic simplification and evaluation @xcite . the lexical complexity prediction task of semeval 2021 @xcite aims at development of systems for prediction of complexity scores for a target word/phrase in a given sentence. in literature, binary classification of target words in a text into complex or non-complex is referred to as complex word identification (cwi) @xcite @xcite @xcite . unlike previous works, a continuous complexity score is assigned to the target word in the present task which is referred to as lexical complexity prediction (lcp) @xcite . for the present work, regression is performed for lcp on a set of linguistic features covering semantic, syntactic and contextual aspects of the target word as described in section 3. additionally, various lexicon based features are used to indicate the rarity of target words. the system achieves 0.8194 pearson correlation for single word target and 0.7482 for multiword target on the trial set. ## task setup the task is divided into two subtasks, namely single word target and multiword target based on the length of the target. the dataset and evaluation metrics are described below. ## features in this section we present the details of the feature space used in the present work. ## single word target in the single word target task, complexity scores between 0 to 1 needs to be assigned for a target word of the input sentence. various regression models are trained using the optimal set of features using scikit-learn foot_1 . the results are presented in ## multiword target in the present task the multiword targets are pairs of two adjacent words. we have experimented with two approaches for predicting complexity scores for multiword targets, as described in section 5.1 and section 5.2 ## conclusion identification of difficult words is an important task for automatic text simplification. lcp aims at assigning scores to words of a given sentence to indicate its complexity. in this work we utilize word level features to capture its lexical, semantic and syntactic information. lm based features are used for indicating the semantics of the target word in a given context. frequency and occurrence based features are used to indicate the overall rarity of the target words. for single word target, random forest regressors trained on the linguistic feature set achieved the highest results. error analysis revealed that the model can be further improved to capture the context of the target word. for multiword target, two approaches were explored. in the first approach complexity scores of individual target words predicted by the sin