  Microblogging sites like Twitter and Weibo have emerged as important
sourcesof real-time information on ongoing events, including socio-political
events, emergency events, and so on. For instance, during emergency events
(such as earthquakes, floods, terror attacks), microblogging sites are very
useful for gathering situational information in real-time. During such an
event, typically only a small fraction of the microblogs (tweets) posted are
relevant to the information need. Hence, it is necessary to design effective
methodologies for microblog retrieval, so that the relevant tweets can be
automatically extracted from large sets of documents (tweets).
  In this work, we apply and compare various neural network-based IR models for
microblog retrieval for a specific application, as follows. In a disaster
situation, one of the primary and practical challenges in coordinating the
post-disaster relief operations is to know about what resources are needed and
what resources are available in the disaster-affected area. Thus, in this
study, we focus on extracting these two specific types of microblogs or tweets
namely need tweets and avail tweets, which are tweets which define some needs
of the people and the tweets which offer some solutions or aid for the people,
respectively.
