One of the most important issues in Information Retrieval is inferring the
intents underlying users' queries. Thus, any tool to enrich or to better
contextualized queries can proof extremely valuable. Entity extraction,
provided it is done fast, can be one of such tools. Such techniques usually
rely on a prior training phase involving large datasets. That training is
costly, specially in environments which are increasingly moving towards real
time scenarios where latency to retrieve fresh informacion should be minimal.
In this paper an `on-the-fly' query decomposition method is proposed.
Micro-blogging services such as Twitter allow anyone to publish anything,
anytime. Nonetheless to say, many of the available contents can be diminished
as babble or spam. However, given the number and diversity of users, some
valuable pieces of information should arise from the stream of tweets. Thus,
such services can develop into valuable sources of up-to-date information (the
so-called real-time web) provided a way to find the most
relevant/trustworthy/authoritative users is available.
Search engines are nowadays one of the most important entry points for
Internet users and a central tool to solve most of their information needs.
Still, there exist a substantial amount of users' searches which obtain
unsatisfactory results. Needless to say, several lines of research aim to
increase the relevancy of the results users retrieve. In this paper the authors
frame this problem within the much broader (and older) one of information
overload.