IIT BHU at FIRE 2017 IRMiDis Track - Fully Automatic Approaches to Information Retrieval

IIT BHU at FIRE 2016 Microblog Track: A Semi-automatic Microblog Retrieval System

This paper presents our work for the Microblog Track in FIRE 2016. The task involved utilizing microblog data (tweets) to retrieve useful information during times of disasters. In particular, given a set of tweets posted during the Nepal earthquake in 2015, the goal was to judge relevance of each tweet against a set of topics which reflected useful information needs. Our approach made use of ma...

Overview of the FIRE 2017 track: Information Retrieval from Microblogs during Disasters (IRMiDis)

The FIRE 2017 Information Retrieval from Microblogs during Disasters (IRMiDis) track focused on retrieval and matching of needs and availabilities of resources from microblogs posted on Twitter during disaster events. A dataset of around 67,000 microblogs (tweets) in English as well as in local languages such as Hindi and Nepali, posted during the Nepal earthquake in April 2015, was made availa...

HLJIT2017-IRMIDIS@IRMiDis-FIRE2017: Information Retrieval from Microblogs during Disasters

Automatic Retrieval of Actionable Information from Disaster-related Microblogs

This paper discusses our work submitted to FIRE 2017 IRMiDis Track [3]. The goal was to extract actionable information from the micro-blogs i.e. tweets which can be leveraged to provide aid and help during disaster events. The two tasks addressed in this work are, first, extraction of useful information such as the need or availability of various resources and second, finding tweets that expres...

DataBros@Information Retrieval from Microblogs during Disasters(IRMiDis)

Microblogging sites like Twitter are increasingly being used for aiding relief operations during disaster events. In such situations, identifying actionable information like needs and availabilities of various types of resources is critical for effective coordination of post disaster relief operations. However, such critical information is usually submerged within a lot of conversational conten...