1 research outputs found
Guiding Corpus-based Set Expansion by Auxiliary Sets Generation and Co-Expansion
Given a small set of seed entities (e.g., ``USA'', ``Russia''), corpus-based
set expansion is to induce an extensive set of entities which share the same
semantic class (Country in this example) from a given corpus. Set expansion
benefits a wide range of downstream applications in knowledge discovery, such
as web search, taxonomy construction, and query suggestion. Existing
corpus-based set expansion algorithms typically bootstrap the given seeds by
incorporating lexical patterns and distributional similarity. However, due to
no negative sets provided explicitly, these methods suffer from semantic drift
caused by expanding the seed set freely without guidance. We propose a new
framework, Set-CoExpan, that automatically generates auxiliary sets as negative
sets that are closely related to the target set of user's interest, and then
performs multiple sets co-expansion that extracts discriminative features by
comparing target set with auxiliary sets, to form multiple cohesive sets that
are distinctive from one another, thus resolving the semantic drift issue. In
this paper we demonstrate that by generating auxiliary sets, we can guide the
expansion process of target set to avoid touching those ambiguous areas around
the border with auxiliary sets, and we show that Set-CoExpan outperforms strong
baseline methods significantly.Comment: WWW 202