This paper present the participation of Information Retrieval Lab(IR LAB DA-IICT Gandhinagar) in FIRE 2016 Microblog Track. The main objective of the track is to identify Information Retrieval methodologies to retrieve important information from Twitter posted during the disasters. We have submitted two runs for this track. In the first run, daiict irlab 1, we have expanded topic term using Wor...

This is the third year that we (ICT-CAS team) participated in the Enterprise Track of TREC. The track of this year includes two tasks, being document search task and expert search task. As to the document search task, we compare two retrieval models, namely BM25 model and language model. As to the expert search task, we focus on the authority of persons by exploring the persons’ recommendation ...

We describe a series of experiments conducted in our participation in the Relevance Feedback Track. We evaluate two traditional weighting models (BM25 and DFR) for the phase 1 task, which are widely used in text retrieval domain. We also evaluate a statistics-based feedback model and our proposed feedback model for the phase 2 task. Currently, we are waiting for the overview paper to facilitate...

Feeding behaviour of giraffe and okapi in captivity can differ significantly from the state in the wild. Duration and complexity of feeding and ruminating behaviour, and total amount of food ingested, are o�en reduced, while the energy content of the diet is increased compared to the wild. As known from domestic ca�le in intensive keeping systems, oral disturbances like tongue-playing or lickin...

The focus of the Okapi TREC{6 Chinese experiments is on investigating the e ectiveness of di erent automatic indexing methods and phrase weighting for retrieval based on probabilistic models over Chinese text. We compare di erent probabilistic weighting methods based on a range of word and single character approaches. There are two indexing methods used in our experiments. One indexing method i...

In this study, we developed a similar text retrieval system using Sentence-BERT (SBERT) for our database of closed medical malpractice claims and investigated its accuracy. We assigned each case in the short Japanese summary accident as well two labels: category was classified hospital department mainly, process indicated failed procedure. evaluated accuracies with labels three different multil...