Improving Sparse Word Representations with Distributional Inference for Semantic Composition
Table 1: Example feature spaces for the lexemes white and clothes extracted from the dependency tree of Figure 1. Not all features are displayed for space reasons. Offsetting ¯¯¯¯¯¯¯¯¯¯¯amod:shoes by amod results in an empty dependency path, leaving just the word co-occurrence :shoes as feature.
['[ITALIC]  [BOLD] white  [BOLD] Distributional Features', '[ITALIC]  [BOLD] white  [BOLD] Offset Features (by amod)', '[ITALIC]  [BOLD] white  [BOLD] Co-occurrence Count', '[ITALIC]  [BOLD] clothes  [BOLD] Distributional Features', '[ITALIC]  [BOLD] clothes  [BOLD] Co-occurrence Count']
For composing the adjective white with the noun clothes via the dependency relation amod we need to consider how the adjective interacts with the noun in the vector space. The distributional features of white describe things that are white via their first order relations such as ¯¯¯¯¯¯¯¯¯¯¯amod, and things that can be done to white things, such as bought via ¯¯¯¯¯¯¯¯¯¯¯amod:¯¯¯¯¯¯¯¯¯¯¯dobj in the example above. However through the inclusion of inverse and higher order dependency paths we can observe that the second order features of the adjective align with the first order features of the noun.