通过正交变换实现跨语言命名实体识别，无需目标语言训练数据。
Language Independent Named Entity Recognition via Orthogonal Transformation of Word Vectors
- 用正交变换将目标语言词向量映射到源语言空间。
- 在英语数据上训练的模型可直接识别阿拉伯语命名实体。
- 适用于低资源语言，尤其适合无标注数据的语言场景。
词嵌入是自然语言处理中的核心组件，许多任务依赖于词嵌入。本文提出一种基于双向LSTM/CRF与词嵌入的命名实体识别模型，可适用于任意语言。该方法在源语言（英语）上训练模型后，通过正交线性变换矩阵将目标语言的词向量转换为源语言词向量空间，从而实现跨语言命名实体识别。实验表明，仅在英语数据集上训练的模型，无需在阿拉伯语数据集上进行训练或微调，即可有效识别阿拉伯语中的命名实体。
原文摘要 · Abstract (English)
Word embeddings have been a key building block for NLP in which models relied heavily on word embeddings in many different tasks. In this paper, a model is proposed based on using Bidirectional LSTM/CRF with word embeddings to perform named entity recognition for any language. This is done by training a model on a source language (English) and transforming word embeddings from the target language into word embeddings of the source language by using an orthogonal linear transformation matrix. Evaluation of the model shows that by training a model on an English dataset the model was capable of detecting named entities in an Arabic dataset without neither training or fine tuning the model on an Arabic language dataset.
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