  Microblogging sites, like Twitter, have emerged as ubiquitous sources of
information. Two important tasks related to the automatic extraction and
analysis of information in Microblogs are Entity Mention Detection (EMD) and
Entity Detection (ED). The state-of-the-art EMD systems aim to model the
non-literary nature of microblog text by training upon offline static datasets.
They extract a combination of surface-level features -- orthographic, lexical,
and semantic -- from individual messages for noisy text modeling and entity
extraction. But given the constantly evolving nature of microblog streams,
detecting all entity mentions from such varying yet limited context of short
messages remains a difficult problem. To this end, we propose a framework named
EMD Globalizer, better suited for the execution of EMD learners on microblog
streams. It deviates from the processing of isolated microblog messages by
existing EMD systems, where learned knowledge from the immediate context of a
message is used to suggest entities. After an initial extraction of entity
candidates by an EMD system, the proposed framework leverages occurrence mining
to find additional candidate mentions that are missed during this first
detection. Aggregating the local contextual representations of these mentions,
a global embedding is drawn from the collective context of an entity candidate
within a stream. The global embeddings are then utilized to separate entities
within the candidates from false positives. All mentions of said entities from
the stream are produced in the framework's final outputs. Our experiments show
that EMD Globalizer can enhance the effectiveness of all existing EMD systems
that we tested (on average by 25.61%) with a small additional computational
overhead.
