Real time project Dot net !!!! "Latent Relational Mapping"

The World Wide Web includes semantic relations of numerous types that exist
among different entities. Extracting the relations that exist between two entities
is an important step in various Web-related tasks such as information retrieval
(IR), information extraction, and social network extraction. A supervised relation
extraction system that is trained to extract a particular relation type (source
relation) might not accurately extract a new type of a relation (target relation) for
which it has not been trained. However, it is costly to create training data manually
for every new relation type that one might want to extract. We propose a
method to adapt an existing relation extraction system to extract new relation
types with minimum supervision. Our proposed method comprises two stages:
learning a lower dimensional projection between different relations, and learning
a relational classifier for the target relation type with instance sampling. First, to
represent a semantic relation that exists between two entities,we extract lexical and syntactic patterns from contexts in which those two
entities co-occur. Then, we construct a bipartite graph between relationspecific
(RS) and relation-independent (RI) patterns. Spectral clustering is
performed on the bipartite graph to compute a lower dimensional projection.
Second, we train a classifier for the target relation type using a small number
of labeled instances. To account for the lack of target relation training
instances, we present a one-sided under sampling method. We evaluate the
proposed method using a data set that contains 2,000 instances for 20 different
relation types. Our experimental results show that the proposed method
achieves a statistically significant macro average F-score of 62.77. Moreover,
the proposed method outperforms numerous baselines and a previously proposed
weakly supervised relation extraction method. www.fu-vision .com
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