We present four problems that will have to be overcome by text co-occurrence programs in order for them to be able to capture human-like semantics. These problems are: the intrinsic deformability of semantic space, the inability to detect co-occurrences of (esp. distal) abstract structures, their lack of essential world knowledge, which humans acquire through learning or direct experience with the world and their assumption of the atomic nature of words. By looking at a number of very simple questions, based in part on how humans do analogy-making, we show just how far one of the best of these programs is from being able to capture real semantics.
Those findings are more deeply analyzed in REF and four main problems are identified to capture human-like semantics: the intrinsic deformability of semantic space, the inability to detect co-occurrences of (especially distal) abstract structures, their lack of essential world knowledge, which humans acquire through learning or direct experience with the world and their assumption of the atomic nature of words.
Four Problems with Extracting Human Semantics from Large Text Corpora
Proceedings of the 24th Annual Conference of the Cognitive Science Society. NJ:LEA
Computer Science