We have also studied the limits of the distant supervision approach to relation extraction, showing empirically that its performance depends not only on the nature of reference knowledge base and document corpus (Riedel et al., 2010), but also on the relation to be extracted.

Given a relation between two arguments, if it is not dominant among textual expressions of those arguments, the distant supervision assumption will be more often violated.

To perform relation extraction, our proposal follows a distant supervision approach (Mintz et al., 2009), which has also inspired other slot filling systems (Agirre et al., 2009; Surdeanu et al., 2010).

Our document-level distant supervision assumption is that if entity and value are found in a document graph (see section 3), and there is a path connecting them, then the document expresses the relation.

Second, the distant supervision assumption underlying our approach is that for a seed relation instance (entity, relation, value), any textual mention of entity and value expresses the relation.

Our system (see Figure l) extracts relational facts from text using distant supervision (Mintz et al., 2009) and then anchors the relation to an interval of temporal validity.

We leverage distant supervision using relations from the knowledge base FreeBase, but do not require any manual heuristic nor manual seed list selections.

We have described a new distant supervision model with which to learn patterns for relation extraction with no manual intervention.

The main contribution of this work is presenting a variant of distance supervision for relation extraction where we do not use heuristics in the selection of the training data.

Similar to other distant supervision methods, our approach takes as input an existing knowledge base containing entities and relations, and a textual corpus.

One of the most important problems to solve in distant supervision approaches is to be able to distinguish which of the textual examples that include two related entities, 67; and 63-, are supporting the relation.

In relation extraction, distant supervision seeks to extract relations between entities from text by using a knowledge base, such as Freebase, as a source of supervision.

We present a novel generative model that directly models the heuristic labeling process of distant supervision .

A particularly attractive approach, called distant supervision (DS), creates labeled data by heuristically aligning entities in text with those in a knowledge base, such as Freebase (Mintz et al., 2009).

Figure 1: Automatic labeling by distant supervision .

The increasingly popular approach, called distant supervision (DS), or weak supervision, utilizes a knowledge base to heuristically label a corpus (Wu and Weld, 2007; Bellare and McCallum, 2007; Pal