***TF-IDF model**:The bag-of-words model works under very simplistic assumptions and at certain times leads to various issues. One of the most common issues is related to some words overshadowing the rest of the words due to very high frequency, as the bag-of-words model utilizes absolute frequencies to vectorize. The **Term Frequency-Inverse Document Frequency** (**TF-IDF**) model mitigates this issue by scaling/normalizing the absolute frequencies. Mathematically, the model is defined as follows:
跳过语法模型体系结构通常尝试实现与 CBOW 模型相反的功能。 它尝试在给定目标词(中心词)的情况下预测源上下文词(环绕词)。 考虑一下前面的简单句子`the quick brown fox jumps over the lazy dog`。 如果我们使用 CBOW 模型,则会得到(`context_window`和`target_word`)对,其中,如果考虑大小为 2 的上下文窗口,则有示例,类似`([quick, fox], brown)`,`([the, brown], quick)`,`([the, dog], lazy)`等。 现在,考虑到跳过语法模型的目的是根据目标单词预测上下文,该模型通常会反转上下文和目标,并尝试根据其目标单词预测每个上下文单词。
Hence, the task becomes to predict the context [*quick*, *fox*] given the target word *brown* or [*the*, *brown*] given the target word *quick*, and so on. Thus, the model tries to predict the `context_window` words based on the `target_word`.