Unlike the previous section, which only uses the vector of the NP head to calculate relatedness, here we include all common nouns occurring before the NP head as well because they also represent the core semantic of an NP (e.g., "earthquake victims" and "the state senate").
given an NP, we first construct a list N which consists of the head and all common nouns appearing before the head, we then represent the NP as a vector v using the following formula, where the suffix " PP" is added to each n if the NP is a bridging anaphor: v = n∈N embeddings P P n |N | (1) Table 3 shows that adding the constraint based on embeddings PP improves the result of MLN model II by 4.5%.