This paper presents a series of methods for automatically determining the gender of proper names, based on their co-occurrence with words and grammatical features in a large corpus. Although the results obtained were for Spanish given names, the method presented here can be easily replicated and used for names in other languages as well. Most methods reported in the literature use pre-existing lists of first names that require costly manual processing and tend to become quickly outdated. Instead, we propose using corpora. Doing so offers the possibility of obtaining real and up-to-date name-gender links. To test the effectiveness of our method, we explored various machine learning methods as well as another method based on simple frequency of co-occurrence. The latter produced the best results: 93% precision and 88% recall on a database of ca. 10,000 mixed names. Our method can be applied to a variety of natural language processing tasks such as information extraction, machine translation, anaphora resolution or large-scale delivery or email correspondence, among others.

本文提出了一系列方法来自动确定专有名词的性别，这是基于它们在大型语料库中与单词和语法特征的共同出现。尽管获得的结果是针对西班牙语给定名称的，但此处介绍的方法可以轻松地复制并用于其他语言的名称。文献中报道的大多数方法都使用预先存在的名字列表，这些名字列表需要昂贵的人工处理，并且往往会很快过时。相反，我们建议使用语料库。这样做提供了获取真实且最新的名称-性别链接的可能性。为了测试我们方法的有效性，我们探索了多种机器学习方法以及基于简单共现频率的另一种方法。后者产生了最好的结果：在ca的数据库中，其准确度为93％，召回率为88％。10 000个混名。我们的方法可以应用于各种自然语言处理任务，例如信息提取，机器翻译，回指解析或大规模传递或电子邮件通信等。