classifier is trained first to distinguish between relation instances and non-relation instances.

Table 4 simplifies a relation instance as a three tuple <Context, M], M2> where the Context includes the Before, Between and After from the

previous research, we used in experiments the nwire (newswire) and bnews (broadcast news) genres of the data containing 348 documents and 4374 relation instances .

The non-relation instances generated were about 8 times more than the relation instances .

The unbalanced distribution of relation instances and non-relation instances remains as an obstacle for pushing the performance of relation extraction to the next level.

At the lexical level, a relation instance can be seen as a sequence of tokens which form a five tuple <Before, M], Between, M2, After>.

Specifically, we first train a binary classifier to distinguish between relation instances and non-relation instances.

Then rather than using the thresholded output of this binary classifier as training data, we use only the annotated relation instances to train a multi-class classifier for the 7 relation types.

In contrast, the kernel based method does not explicitly extract features; it designs kernel functions over the structured sentence representations (sequence, dependency or parse tree) to capture the similarities between different relation instances (Zelenko et al., 2003; Bunescu and Mooney, 2005a; Bunescu and Mooney, 2005b; Zhao and Grishman, 2005; Zhang et al., 2006; Zhou et al., 2007; Qian et al., 2008).

The assumption is that even if the word soldier may never have been seen in the annotated Employment relation instances , other words which share the same cluster membership with soldier such as president and ambassador may have been observed in the Employment instances.

diversity in the discovered relation types by restricting the number of times a single word can serve as either an indicator or part of the argument of a relation instance .

First, the model’s generative process encourages coherence in the local features and placement of relation instances .

To incorporate training examples in our model, we simply treat annotated relation instances as observed variables.

For finance, it takes at least 10 annotated documents (corresponding to roughly 130 annotated relation instances ) for the CRF to match the semi-supervised model’s performance.

For earthquake, using even 10 annotated documents (about 71 relation instances ) is not sufficient to match our model’s performance.

As in other information extraction systems, the category and relation instances extracted by NELL contain polysemous and synonymous noun phrases.

Both extracting more relation instances and adding new relations to the ontology will improve synonym res-

The main input to ConceptResolver is a set of extracted category and relation instances over noun phrases, like person(:c1) and ceoOf(:c1, :52), produced by running NELL.