The data was first tagged with the Stanford NER system (Finkel et al., 2005) and then entity mentions were found by collecting each continuous phrase where words were tagged identically (i.e., as a person, location, or organization).

These include indicators for various lexical, part of speech, named entity, and dependency tree path properties of entity mentions in specific sentences, as computed with the Malt dependency parser (Nivre and Nilsson, 2004) and OpenNLP POS taggerl.

As input we have (1) E, a set of sentences, (2) E, a set of entities mentioned in the sentences, (3) R, a set of relation names, and (4) A, a database of atomic facts of the form 7“(€1, 62) for 7“ E R and 6,- E E. Since we are using weak learning, the Y7" variables in Y are not directly observed, but can be approximated from the database A.

(2) E, a set of entities mentioned in the sentences, (3) R, a set of relation names, and

An entity mention is a contiguous sequence of textual tokens denoting an entity.

In this paper we assume that there is an oracle which can identify all entity mentions in a corpus, but the oracle doesn’t normalize or disambiguate these mentions.

A relation mention is a sequence of text (including one or more entity mentions ) which states that some ground fact r(e) is true.

A relation was defined over a pair of entity mentions within a single sentence.

The heads of the two entity mentions are marked.

As a relation in ACE is usually short, the words of the two entity mentions can provide more critical indications for relation classification than the words from the context.

Within the two entity mentions , the head word of each mention is usually more important than other words of the mention; the conjunction of the two heads can provide an additional clue.

And in general words other than the chunk head in the context do not contribute to establishing a relationship between the two entity mentions .

Following previous work, we did 5-fold cross-validation on the 348 documents with hand-annotated entity mentions .

Given a pair of entity mentions < m. , m j > and the sentence containing the pair, a feature based system extracts a feature vector v which contains diverse lexical, syntactic and semantic features.