论文标题
在作者身份归因中提高单词频率
Boosting word frequencies in authorship attribution
论文作者
论文摘要
在本文中，我引入了一种简单的方法，用于计算作者归因和类似式式任务的相对单词频率。我认为，将相对频率计算为给定单词的出现数量除以文本中的代币数量，而是说一个更有效的归一化因子仅是相关令牌的总数。相关词的概念包括同义词，通常在某些方面与所讨论的单词相似，通常几十个其他单词。为了确定这种语义背景，可以使用一个单词嵌入模型。所提出的方法的表现优于经典的最频繁的方法，通常取决于输入设置，通常要以几个百分点的点。
In this paper, I introduce a simple method of computing relative word frequencies for authorship attribution and similar stylometric tasks. Rather than computing relative frequencies as the number of occurrences of a given word divided by the total number of tokens in a text, I argue that a more efficient normalization factor is the total number of relevant tokens only. The notion of relevant words includes synonyms and, usually, a few dozen other words in some ways semantically similar to a word in question. To determine such a semantic background, one of word embedding models can be used. The proposed method outperforms classical most-frequent-word approaches substantially, usually by a few percentage points depending on the input settings.
