Searches for information on the web involving both geographic (“where”) and temporal (“when”) components comprise a nontrivial percentage of overall searches. In this paper, we describe an approach to identifying specific documents within a collection that satisfy a set of geo-temporal queries. To test our approach, we submitted five runs to NTCIR-8 GeoTime, using the Indri search engine, on a ...

Evaluation measures play a vital role in analyzing the performance of a system, comparing two or more systems, and optimizing systems to perform some task. In this paper, we analyze and highlight the strengths and weaknesses of commonly used measures for evaluating the diversity in search results. We compare MAP-IA, α-nDCG, and ERR-IA using data from TREC’09 web track diversity runs and simulat...

Structured tasks are distinctive: each task has its own measure of performance, such as the word error rate in speech recognition, the BLEU score in machine translation, the NDCG score in information retrieval, or the intersection-over-union score in visual object segmentation. This paper presents StructED, a software package for learning structured prediction models with training methods that ...

The Andd7 team from Dhirubhai Ambani Institute of Information and Communication Technology(DA-IICT) participated in both the subtasks namely Temporal Query Intent Classification(TQIC) and Temporal Information Retrieval(TIR) of the pilot task of NTCIR-11 Temporal Information Access(Temporalia) Task [4]. This report describes different classification methods and feature sets used for classifying ...

A spoof detection algorithm supports the speaker verification system to examine false claims by an imposter through careful analysis of input test speech. The scores are employed categorize genuine and spoofed samples effectively. Under mismatch conditions, acceptance ratio increases can be reduced appropriate score normalization techniques. In this article, we using normalized Discounted Cumul...

In this paper, we describe our submission to the NTCIR12 Short Text Conversation task. We consider short text conversation as a community Question-Answering problem, hence we solve this task in three steps: First, we retrieve a set of candidate posts from a pre-built indexing service. Second, these candidate posts are ranked according to their similarity with the original input post. Finally, w...

This article summarizes the approach developed for TREC 2016 Clinical Decision Support Track. In order to address the daunting challenge of retrieval of biomedical articles for answering clinical questions, an information retrieval methodology was developed that combines pseudo-relevance feedback, semantic query expansion and document similarity measures based on unsupervised word embeddings. T...

This paper describes the participation of the CBNU team at the TREC Clinical Decision Support track 2015. We propose a query expansion method based on a clinical semantic knowledge and a topic model. The clinical semantic knowledge is constructed by using medical terms extracted from Unified Medical Language System (UMLS) and Wikipedia articles. The word and document topics are generated by usi...

Many existing information retrieval models do not explicitly take into account information about word associations. Our approach makes use of first and second order relationships found in natural language, known as syntagmatic and paradigmatic associations, respectively. This is achieved by using a formal model of word meaning within the query expansion process. On ad hoc retrieval, our approac...

While patients can freely access their Electronic Health Records or online health information, they may not be able to correctly understand the content of these documents. One of the challenges is related to the difference between expert and non-expert languages. We propose to investigate this issue within the Information Retrieval field. The patient queries have to be associated with the corre...