When encounter a mention, we map it to a set of mention senses, and then find the nearest one according to the distance from its context vector to each mention sense cluster center, which will be discussed in Section 5.
4 Considering all the above representation learning components, we define the overall objective function as linear combinations: we approximately assign each mention independently: where P (C(m l )|s l j ) is proportional to cosine similarity between context vector and mention sense cluster center µ l j to measure the mention's local similarity, namely local probability.