Detecting the Need for Resources and their Availability

In this working note we describe our submission to the FIRE2017 IRMiDiS track. We participated in both sub-tasks, the first of which was directed at identifying the need or availability of specific resources and the second at matching tweets expressing the need for a resource with tweets mentioning their availability. Our linguistically motivated approach using pattern matching of word n-grams achieved an overall average MAP score of 0.2458 for sub-task (1), outperforming ourmachine-learning approach (MAP 0.1739)while being surpassed by two other (automatic) systems. The linguistic approach was also used in sub-task (2). There it was the bestperforming approach with an f-score of 0.3793.