Cosine similarity measures how much two vectors point in the same direction, ignoring how long they are. A score of 1 means they point the same way, exactly aligned. A score of -1 means they point in opposite directions. A score of 0 means the vectors are perpendicular to each other, with no directional alignment between their features. In other words, there’s no shared orientation in the space the vectors live in, which is what we mean by no similarity in their directions. Opposite meanings would correspond to a cosine similarity of -1, not 0. Identical meaning would be 1, and moderate similarity would be a positive value between 0 and 1.

Context-dependent embeddings are dynamic vector representations of words that vary with surrounding context. Unlike static embeddings, where each word has a single fixed vector, contextualized embeddings are computed for each occurrence by incorporating the other words in the sentence (and sometimes the broader text) through mechanisms like self-attention in transformers. This means the same word can have different representations in different sentences, which helps resolve nuances and multiple meanings of a word, such as “bank” in “river bank” versus “financial bank.” Modern models like BERT or GPT produce these contextual embeddings, reflecting how language usage changes with context. The other options don’t fit because one describes static embeddings that ignore context, another wrongly limits the concept to image data, and the last describes model compression, which is unrelated to how word representations are formed.

The main idea is that producing fluent language output can be done without actually understanding the language. In the Chinese Room thought experiment, a person who doesn’t know Chinese follows a precise set of rules to transform symbols and generate convincing Chinese replies. To an external observer, the responses read as fluent understanding, but the person inside the room has no grasp of meaning. The takeaway is that syntactic symbol manipulation can yield convincing language without semantic comprehension, so fluent output alone does not prove true understanding. That’s why the option stating that a system with fluent outputs may still lack understanding is the best fit. The other ideas misstate the point: a simple lookup mechanism could, in principle, produce fluent responses, so the claim isn’t about look-up tables versus fluency; having perception or motor abilities does not by itself guarantee understanding; and while some argue about the role of consciousness, the Chinese Room primarily targets the distinction between appearing to understand and actually understanding, not a definitive claim about consciousness being required.

Meaning, in this view, comes from how a word is used in practice. The Associationist perspective sees words as tied to learned associations with situations, actions, and responses; the sense of a word unfolds as we encounter it in different contexts and rely on those contexts to guide how we use it again. In other words, what a word means isn’t fixed by a dictionary entry or by grammar alone, but by the way it functions in real language—the actions, expectations, and social purposes it enables. Wittgenstein’s remark that meaning is the use of a word in the language captures this idea: meaning is rooted in use, not in an abstract definition. This emphasis on use and context helps explain how words can shift meaning across situations while still aligning with the same overall familiar usage.