论文标题
命名实体识别中的情境化嵌入：一项概括的实证研究
Contextualized Embeddings in Named-Entity Recognition: An Empirical Study on Generalization
论文作者
论文摘要
上下文化的嵌入使用无监督的语言模型根据其上下文计算单词表示。这对于概括在直觉上很有用，尤其是在指定性识别中，在训练中从未见过的提及至关重要。但是，标准英语基准高估了词汇对上下文特征的重要性，因为火车和测试提及之间存在不现实的词汇叠加。在本文中，我们通过通过新颖性和外域评估来分开提及，对最新的上下文化嵌入的概括能力进行经验分析。我们表明，它们对于看不见的提及探测特别有益，尤其是域外。对于在CONLL03训练的模型，语言模型上下文化导致 +1.2％最大相对MICRO-F1得分在WNUT数据集中与 +13％的域外域增加域内增加
Contextualized embeddings use unsupervised language model pretraining to compute word representations depending on their context. This is intuitively useful for generalization, especially in Named-Entity Recognition where it is crucial to detect mentions never seen during training. However, standard English benchmarks overestimate the importance of lexical over contextual features because of an unrealistic lexical overlap between train and test mentions. In this paper, we perform an empirical analysis of the generalization capabilities of state-of-the-art contextualized embeddings by separating mentions by novelty and with out-of-domain evaluation. We show that they are particularly beneficial for unseen mentions detection, especially out-of-domain. For models trained on CoNLL03, language model contextualization leads to a +1.2% maximal relative micro-F1 score increase in-domain against +13% out-of-domain on the WNUT dataset
