# RGloVe: An Improved Approach of Global Vectors for Distributional Entity Relation Representation
## 1 Introduction  
With the explosive growth and easy accessibility of web documents, extracting the useful nuggets from the irrelevant and redundant messages becomes a cognitively demanding and time consuming task. Under this circumstance, information extraction is proposed to extract the structured data from text documents. The automatic content extraction (ACE) program [1] provides annotated corpus and evaluation criteria for a series of information extraction tasks. As an important level of information extraction, relation extraction aims to extract the relationships between named entities. Relation extraction is widely used in many fields such as automatic construction of knowledge base and information retrieval.  
Traditional relation extraction is often limited to extracting the pre-defined types. For example, ACE 2003 defines five relation types, including AT (location relationships), NEAR (to identify relative locations), PART (part-whole relationships), ROLE (the role a person plays in an organization) andSOCIAL (such as parent and sibling), which are further separated into 24 relation sub-types. However, traditional relation extraction leaves the question open whether it is still efficient for massive and heterogeneous corpora such as web documents [2; 3; 4]. To combat these problems, many researches have been done to extract more abundant relations without requiring any specific relation type or a vocabulary such as verbs [5; 6; 7; 8].  
In light of the above, this paper aims to answer the following research questions:  
* How to learn the distributional entity representations only by using the statistical information of entity-entity co-occurrences?
* How to build, store and query the relationships between entities without extracting the predefined relation types or vocabulary?  
To answer above questions, we have made the following contributions in this paper. For RQL, this paper presents an improved model of global vectors called RGloVe based on the idea of distributed representation. Global vectors (GloVe) [9] is an effective method to train distributional word representations from the global statistics of word occurrences in the whole corpus. In order to train model more efficiently, we improve the traditional GloVe model by using cosine similarity between entity vectors to approximate the entity occurrences instead of dot product. Because cosine similarity can convert vector to unit vector, it is intuitively more reasonable and more easily converge to a local optimum. For RQ2, instead of extracting relation types or a vocabulary, the task of distributional relation representation aims to extract a series of triples \((e_{1},e_{2},\omega)\). The weight \(\omega\) is a real value which indicates the correlation of two entities \(e_{1}\) and \(e_{2}\). In order to store these triples and facilitate their retrival, we introduce a graph database of Neo4j where nodes represent the entities and edges represent the relationships between entities. The cypher query language of Neo4j provides a highly accessible way to query different levels of relationships (e.g., friends of a friend).  
The rest of this paper is as follows. We review related work in traditional relation extraction and distributional word representation in Section 2. In Section 3, we will present all the details of Neo4j and RGloVe. Section 4 shows the experiment results of quantitative representation between named entities. Finally, we conclude our work and point out future work in Section 5.