论文《A Generative Entity-Mention Model for Linking Entities with Knowledge Base》

A Generative Entity-Mention Model for Linking Entities with Knowledge Base

 

一．主要方法

提出了一种生成概率模型，叫做entity-mention model.

Explanation:

In our model, each name mention to be linked is modeled as a sample generated through a three-step generative story, and the entity knowledge is encoded in the distribution of entities in document P(e), the distribution of possible names of a specific entity P(s|e), and the distribution of possible contexts of a specific entity P(c|e). To find the referent entity of a name mention, our method combines the evidences from all the three distributions P(e), P(s|e) and P(c|e).

The P(e), P(s|e) and P(c|e) are respectively called the entity popularity model, the entity name model and the entity context model

二．相关介绍

建模

Given a set of name mentions M = {m1, m2, …, mk} contained in documents and a knowledge base KB containing a set of entities E = {e1, e2, …, en}, an entity linking system is a function s : M ® E which links these name mentions to their referent entities in KB.

Popularity Knowledge

实体的流行度知识告诉我们一个实体出现在文档中的可能性

Name Knowledge

名称知识告诉我们实体的可能名称，以及名称引用特定实体的可能性。

Context Knowledge

上下文知识告诉我们一个实体出现在特定上下文中的可能性。

三．The Generative Entity-Mention Model for Entity Linking

Explanation

1. 首先，该模型根据P(e)中实体的分布情况，从给定知识库中选择提及名称的引用实体e。
2. 其次，该模型根据被引用实体P(s|e)的可能名称的分布情况输出所述名称的名称s。
3. 最后，模型根据被引用实体P(c|e)可能的上下文分布输出所提到的名称的上下文c。

model

The probability of a name mention m (its context is c and its name is s) referring to a specific entity e can be expressed as the following formula (here assume that s and c are independent):

Give a name mention m, to perform entity linking, we need to find the entity e which maximizes the probability P(e|m).

　　　　　　　　　　　　　　 

Candidate Selection

building a name-to-entity dictionary using the redirect links, disambiguation pages, anchor texts of Wikipedia, then the candidate entities of a name mention are selected by finding its name’s corresponding entry in the dictionary

四．Model Estimation

Entity Popularity Model

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where Count(e) is the count of the name mentions whose referent entity is e, and the |M| is the total name mention size.

Entity Name Model

比如，我们希望 P(Michael Jordan|Michael Jeffrey Jordan) 高，,P(MJ|Michael Jeffrey Jordan) 也高。 P(Michael I. Jordan|Michael Jeffrey Jordan) 应该是0.

因此，名称模型可以通过首先从数据集中收集所有(实体、名称)对来估计。

缺点：它不能正确地处理一个不可见的实体或一个不可见的名称。

Eg: “MJ”在Wikipedia指的并不是Michael Jeffrey Jordan, 这个the name model 将不能识别 “MJ” 就是Michael Jeffrey Jordan.

　　　　↓

1) It is retained (translated into itself);

2) It is translated into its acronym;

3) It is omitted(translated into the word NULL);

4) It is translated into another word (misspelling or alias).

wheree is a normalization factor, f is the full name of entity e, lf is the length of f, ls is the length of the name s, si the i th word of s, fj is the j th word of f and t(si|fj) is the lexical translation probability which indicates the probability of a word fj in the full name will be written as si in the output name.

Entity Context Model

例如：

C1: __wins NBA MVP.

C2: __is a researcher in machine learning

P(C1|Michael Jeffrey Jordan)应该很高，因为NBA球员迈克尔杰弗里乔丹经常出现在C1和P(C2|Michael Jeffrey Jordan)应该是非常低的，因为他很少出现在C2.

a context c containing n terms t1,t2…tn (term: a word; a named entity; a Wikipedia concept) ,the entity context model estimates the probability P(c|e) as

　　　　　　　　　　　　　　　　　　

where Pg(t) is a general language model which is estimated using the whole Wikipedia data, and the optimal value of λ is set to 0.2

　　　　　　　　　　　　　　　　　　　　 

where Counte(t) is the frequency of occurrences of a term t in the contexts of the name mentions whose referent entity is e

The NIL Entity Problem

假设：“如果一个名字被提到是指一个特定的实体，那么这个名字被提到的概率是由特定实体的模型产生的，应该显著高于由一般语言模型产生的概率

1. add a pseudo entity, the NIL entity, into the knowledge base

2. the probability of a name mention is generated by the NIL entity is higher than all other entities in Knowledge base, we link the name mention to the NIL entity.

五．Experiments