| Composition methods | Distributional semantic models (DSMs), also known as vector-space models, semantic spaces , or by the names of famous incarnations such as Latent Semantic Analysis or Topic Models, approximate the meaning of words with vectors that record their patterns of co-occurrence with corpus context features (often, other words). |
| Experimental setup | tion is that a vector, in order to be a good representation of the meaning of the corresponding word, should lie in a region of semantic space populated by intuitively similar meanings, e. g., we are more likely to have captured the meaning of car if the NN of its vector is the automobile vector rather than potato. |
| Experimental setup | All 900 derived vectors from the test set were matched with their three closest NNs in our semantic space (see Section 4.2), thus producing a set of 2, 700 word pairs. |
| Experimental setup | 6Most steps of the semantic space construction and composition pipelines were implemented using |