This paper describes a system submitted by team BigGreen to LCP 2021 for
predicting the lexical complexity of English words in a given context. We
assemble a feature engineering-based model with a deep neural network model
founded on BERT. While BERT itself performs competitively, our feature
engineering-based model helps in extreme cases, eg. separating instances of
easy and neutral difficulty. Our handcrafted features comprise a breadth of
lexical, semantic, syntactic, and novel phonological measures. Visualizations
of BERT attention maps offer insight into potential features that Transformers
models may learn when fine-tuned for lexical complexity prediction. Our
ensembled predictions score reasonably well for the single word subtask, and we
demonstrate how they can be harnessed to perform well on the multi word
expression subtask too.

本文介绍一个系统，该系统可以预测英语单词在给定上下文中的词汇复杂度。该系统基于 BERT，并通过特征工程模型与深度神经网络模型相结合，使用手工制作的特征来进行预测。通过 BERT 注意力图的可视化，我们可以发现 Transformers 模型在进行预测时所学习的一些特征。我们展示了如何将其用于多单词表达式子任务，同时，对于单一单词子任务，我们的集成预测表现也十分出色。

BigGreen 在 SemEval-2021 任务 1 中使用装配模型预测词汇复杂度

BigGreen at SemEval-2021 Task 1: Lexical Complexity Prediction with  Assembly Models

We present the New York Times Word Innovation Types dataset, or NYTWIT, a
collection of over 2,500 novel English words published in the New York Times
between November 2017 and March 2019, manually annotated for their class of
novelty (such as lexical derivation, dialectal variation, blending, or
compounding). We present baseline results for both uncontextual and contextual
prediction of novelty class, showing that there is room for improvement even
for state-of-the-art NLP systems. We hope this resource will prove useful for
linguists and NLP practitioners by providing a real-world environment of novel
word appearance.

本研究介绍 New York Times Word Innovation Types dataset（NYTWIT）数据集，包括超过 2500 个英语新词汇，手动标注其创新类型，希望为语言学家和 NLP 从业者提供新词汇出现的真实环境，并且展示了对于 NLP 系统的非上下文和上下文预测的基线结果，表明仍有提高空间。