What are the limitations of TF-IDF?

The main limitation of TF-IDF is that its scores do not directly represent how important a word is to understanding a text. It treats terms independently, does not understand word meanings or context, and ignores word order. As a result, it may struggle to recognize synonyms or understand phrases made up of multiple words. In addition, extremely rare terms can have misleadingly high IDF values. When interpreting results, compare the scores with the original text rather than assuming that high-scoring terms are central to the content or that differences in wording imply differences in meaning.

Synonyms are counted separately, so scores may not reflect similarities in meaning

TF-IDF treats each term independently, which can make it difficult to recognize synonyms and similar words. Even when two passages discuss similar concepts, their scores may not reflect that connection if they use different wording.

Suppose one passage uses “bicycle” and another uses “bike.” If these are treated as separate terms, TF-IDF itself cannot be expected to know that they may refer to the same thing. This does not mean that all results will be distorted. Rather, it is a reminder that differences between terms do not necessarily imply differences between concepts.

TF-IDF does not understand context and ignores word order, so scores alone cannot reveal a sentence’s meaning

TF-IDF also lacks an understanding of word meanings and context. The same word can have different meanings in different passages, but calculating its score does not mean TF-IDF understands what it refers to in a particular passage. Take “apple,” for example: the term’s score alone cannot tell you whether the article discusses the fruit or the brand. You still need to read the surrounding text.

Word order is another blind spot. The source notes that TF-IDF ignores word order and cannot understand the overall meaning of compound nouns or phrases on its own. When interpreting results, check:

• What the word actually refers to in the original text, rather than just how high or low its score is.
• Whether it belongs to a phrase and whether reading it in isolation would obscure the intended meaning.
• Whether words that appear together need to be understood in terms of their order and context.

A high IDF for an extremely rare term does not necessarily mean it is important

The source also cautions that extremely rare terms may receive misleadingly high IDF values, making them appear more important than they actually are. In other words, “rare” does not automatically mean “important,” but this does not mean rare terms necessarily lack value.

For example, suppose an article contains an extremely uncommon name. Even if its IDF is high, you still need to check whether it is part of the article’s main subject or appears only in a side discussion. A high IDF can prompt a closer look at a term, but it does not prove that the term is important to understanding the text. Likewise, a term’s rarity alone does not mean its score must be misleading.