Our method exploits the data co-occarrence phenomena that is very useful for relation extractions .

Table 4: F all-abbreviation Relation Extraction Precision

To further show the advantage of our relation extraction algorithm (see Section 3.3), in the third column of Table 4 we report the results on a simple baseline.

As shown in Table 4, the baseline performs significantly worse than our relation extraction algorithm.

3.3 F all-abbreviation Relation Extraction from Chinese Monolingual Corpora

3.3.2 F all-abbreviation Relation Extraction Algorithm

Figure 2 presents the pseudocode of the full-abbreviation relation extraction algorithm.

raditional Relation Extraction

We also plan to explore the capacity of Open IE to automatically provide labeled training data, when traditional relation extraction is a more appropriate choice.

4.2 Stacked Relation Extraction

Relation Extraction (RE) is the task of recognizing the assertion of a particular relationship between two or more entities in text.

Given a relation name, labeled examples of the relation, and a corpus, traditional Relation Extraction (RE) systems output instances of the given relation found in the corpus.

Figure 1: Relation Extraction as Sequence Labeling: A CRF is used to identify the relationship, born in, between Kafka and Prague

Linear-chain CRFs have been applied to a variety of sequential text processing tasks including named-entity recognition, part-of—speech tagging, word segmentation, semantic role identification, and recently relation extraction (Culotta et al., 2006).

In this section, we show that many relationships are consistently expressed using a compact set of relation-independent lexico-syntactic patterns, and quantify their frequency based on a sample of 500 sentences selected at random from an IE training corpus developed by (Bunescu and Mooney, 2007).1 This observation helps to explain the success of open relation extraction , which learns a relation-independent extraction model as described in Section 3.1.

We further show that we can use these paraphrases to generate surface patterns for relation extraction .

While we believe that more work needs to be done to improve the system recall (some of which we are investigating), this seems to be a good first step towards developing a minimally supervised, easy to implement, and scalable relation extraction system.

5.3 Relation Extraction

Moving to the task of relation extraction , we see from table 5 that our system has a much lower relative recall compared to the baseline.

Claim 2: These paraphrases can then be used for generating high precision surface patterns for relation extraction .

Another task related to our work is relation extraction .