The construction of the semantic space depends on the definition of linguistic context (e.g., neighbour-ing words can be documents or collocations), the number of components used (e.g., the k most frequent words in a corpus), and their values (e.g., as raw co-occurrence frequencies or ratios of probabilities).

A hypothetical semantic space is illustrated in Figure 1.

1A detailed treatment of existing semantic space models is outside the scope of the present paper.

This change in the verb’s sense is equated to a shift in its position in semantic space .

Model Parameters Irrespectiver of their form, all composition models discussed here are based on a semantic space for representing the meanings of individual words.

The semantic space we used in our experiments was built on a lemmatised version of the BNC.

Moreover, the vector similarities within such semantic spaces have been shown to substantially correlate with human similarity judgments (McDonald, 2000) and word association norms (Denhire and Lemaire, 2004).

Figure l: A hypothetical semantic space for horse and run

We represent the semantic space for verbs as a matrix of frequencies, where each row corresponds to a Levin verb and each column represents a given feature.

We construct a semantic space with each feature set.

For instance, the semantic space with CO features contains over one million columns, which is too huge and cumbersome.