hub at S em E val-2021 Task 1: Fusion of Sentence and Word Frequency to Predict Lexical Complexity (2021)

Abstract: In this paper, we propose a method of fusing sentence information and word frequency information for the SemEval 2021 Task 1-Lexical Complexity Prediction (LCP) shared task. In our system, the sentence information comes from the RoBERTa model, and the word frequency information comes from the Tf-Idf algorithm. Use Inception block as a shared layer to learn sentence and word frequency information We described the implementation of our best system and discussed our methods and experiments in the task. The shared task is divided into two sub-tasks. The goal of the two sub-tasks is to predict the complexity of a predetermined word. The shared task is divided into two subtasks. The goal of the two subtasks is to predict the complexity of a predetermined word. The evaluation index of the task is the Pearson correlation coefficient. Our best performance system has Pearson correlation coefficients of 0.7434 and 0.8000 in the single-token subtask test set and the multi-token subtask test set, respectively.

## introduction and background language and writing are the main ways we transmit knowledge and information. an accurate and efficient understanding of the meaning expressed in the text is of great significance to our learning and production. vocabulary complexity and reading comprehension are inextricably linked, and overly complex terms may bring bad results @xcite . the research of @xcite showed that the use of vocabulary simplification technology is one of the ways to effectively improve readers' reading comprehension ability @xcite . accurately predicting lexical complexity can make the system better guide users to use appropriate text, or customize text according to their needs. especially when some ordinary readers are reading technical text content @xcite . lexical complexity detection and complex lexical simplification have attracted the attention of the nlp community, and systems have been developed to simplify the text of second language learners @xcite , native speakers with low literacy levels @xcite , and people with dyslexia @xcite . the topic of the shared task of semeval 2021 task 1 is "lexical complexity prediction (lcp)". the task data set uses english text data in a single language @xcite . there are two subtasks in the task, which are the subtasks for predicting the complexity of a single token and multiple tokens @xcite . in this article, we give the method and task result of predicting word complexity. our system uses a method that combines sentence, word frequency, and context information. the acquisition of sentences and context information uses the roberta model @xcite . the word frequency information comes from the tf-idf algorithm @xcite . the complexity is a continuous value, so the whole task can be regarded as a regression task. we provide the model code used in this task foot_0 . ## related work previously held similar to this shared task are se-meval 2016 task 11: complex word identification (cwi2016) @xcite , complex word identification shared task 2018 (cwi2018) @xcite . in cwi2016, the system voting method used by paetzold et al. has achieved excellent results in sharing tasks @xcite . in cwi2018, butnaru uses a kernel-based learning method for complex word identification @xcite . sian and other methods using integrated voting have also achieved good scores @xcite . in addition to the above methods, some common methods are applied to these tasks. for example, svm, random forest, artificial neural network system, naive bayes, decision tree, etc. (paetzold and specia, 2016a; yimam et al., 2018). ## experiment and results in this section, we will introduce the data preprocessing methods and experimental settings we used in the task and the final results. ## results and analysis according to the pearson correlation coefficient, the results submitted by the teams participating in the two subtasks are ranked. in the published results, the task organizer team also announced some other evaluation indicators. these evaluation indicators are spearman correlation (rho), mean absolute error (mae), mean squared error (mse), r-squared (r2). we compare the scores of pearson correlation coefficient results obtained by several different methods proposed in the experimental part. the results of these different methods can be found in after comparing the result scores of different systems on the verification set, we used the roberta+tfifd+inception system to predict the results of the test set and successfully submitted it to the task organizer team. our test set prediction result scores are 38th and 22nd respectively in the ## conclusion in this article, we describe the system our team has developed for shared tasks in semeval 2021 task1 lcp. the system combines lexical, syntactic, and contextual semantic features. we describe and analyze the tasks, data, experiments, and results. we compared the results of the roberta model and the bert model. in the final test set prediction result score ranking, our results in the competition ranked middle. in future work, we will study how the complexity of the phrase is affected by the context in the sentence. for our model and method, we can also try to introduce other types of word embedding, and use different models to fuse the output of roberta and the output of word embedding.