TF – IDF for Bigrams & Trigrams
TF-IDF in NLP stands for Term Frequency – Inverse document frequency. It is a very popular topic in Natural Language Processing which generally deals with human languages. During any text processing, cleaning the text (preprocessing) is vital. Further, the cleaned data needs to be converted into a numerical format where each word is represented by a matrix (word vectors). This is also known as word embedding
Term Frequency (TF) = (Frequency of a term in the document)/(Total number of terms in documents)
Inverse Document Frequency(IDF) = log( (total number of documents)/(number of documents with term t))
TF.IDF = (TF).(IDF)
NLP 中的 TF-IDF 代表詞頻 – 逆文檔頻率。這是自然語言處理中一個非常流行的話題，通常涉及人類語言。在任何文本處理過程中，清理文本（預處理）至關重要。此外，清洗後的數據需要轉換為數字格式，其中每個詞都由矩陣（詞向量）表示。這也稱為詞嵌入
詞頻 (TF) =（文檔中詞的頻率）/（文檔中詞的總數）
逆文檔頻率（IDF）= log（（文檔總數）/（文檔總數）帶有術語 t)) 的文檔
TF.IDF = (TF).(IDF)
Bigrams： Bigram 是一個句子中的 2 個連續單詞。
Trigrams： Trigram 是一個句子中的 3 個連續單詞。
