Given neighbor mentions N (m i ), we extract two types of vectorial global features and structured global features for each candidate e j ∈ Φ(m i ): Neighbor Mention Compatibility Suppose neighbor mentions are topical coherent, a candidate entity shall also be compatible with neighbor mentions if it has a high compatibility score with the current mention, otherwise not.
we extract the vectorial global features by computing the similarities between e j and all neighbor mentions: {sim(e j , m i )|m i ∈ N (m i )}, where m i is the mention embedding by averaging the global vectors of words in its surface form: are tokenized words of mention m j .