When using non-semantic approaches to model text — such as representing documents as vectors of word counts or weighted scores — you may encounter situations where these models fail to capture the true relationships between documents. One common scenario is when two documents discuss the same topic but use very different sets of words. For example, if one document uses "car" and another uses "automobile," a simple vector model will treat these as unrelated, even though they mean the same thing. As a result, documents that are closely related in meaning may appear distant in the vector space, leading to poor clustering or retrieval results.

Another issue arises when a single word has multiple meanings, but the model cannot distinguish which meaning is intended in each document. For instance, the word bank could refer to a financial institution or the side of a river. If two documents use the word bank but in different contexts, a vector model will still consider them similar based solely on the shared word, even though the actual topics may be unrelated. This lack of context can cause documents to cluster together incorrectly or be considered similar when they are not.

It is important to recognize the boundaries of what non-semantic, vector-based modeling can achieve. These approaches are powerful for tasks where surface-level word overlap is a good indicator of similarity, but they cannot capture deeper relationships that depend on meaning, context, or language structure. You should always consider these limitations when interpreting the results of clustering or similarity analysis using non-semantic models, and be cautious about drawing strong conclusions from patterns that may arise due to the model's assumptions rather than true document relationships.