Data envelopment analysis (DEA) is a powerful technique for performance evaluation of decision making units (DMUs). One of the main objectives that is followed in performance evaluation is discriminating among efficient DMUs to provide a complete ranking of DMUs. DEA successfully divides them into two categories: efficient DMUs and inefficient DMUs. The DMUs in the efficient category have ident...

This paper reports the systems we submitted to the Microblog Track shared in TREC 2014 which focuses on ad hoc retrieval (i.e., retrieving top 1, 000 relevant tweet for every given topic). To address this task, we adopted a two-stage framework, i.e., firstly, we performed query expansion (i.e., expanding relevant inforamtion using pseudorelevance feedback and Google search engine results) to re...

Today, the World Wide Web is the popular and interactive medium to access various types of information in different fields. The web contains huge amount of information and most of the user access internet for information retrieval. But, most of the time user gets irrelevant results on search engine after firing any query. So, for better accuracy and relevant results according to user requiremen...

We investigate the influence of information status (IS) on constituent order in German, and integrate our findings into a loglinear surface realisation ranking model. We show that the distribution of pairs of IS categories is strongly asymmetric. Moreover, each category is correlated with morphosyntactic features, which can be automatically detected. We build a loglinear model that incorporates...

Graph-based manifold-ranking methods have been successfully applied to topic-focused multi-document summarization. This paper further proposes to use the multi-modality manifold-ranking algorithm for extracting topic-focused summary from multiple documents by considering the within-document sentence relationships and the cross-document sentence relationships as two separate modalities (graphs)....

In this paper, we evaluate a generic network-based inference algorithm for Web API recommendation. Based on experimental data collected from the Programmable Web repository, we construct two tripartite networks: one where the nodes are Web APIs, users and mashups, and another where the nodes are Web APIs, users and tags. Experimental results show that the network-based inference algorithm yield...

We present a domain-independent clusteringbased approach for automatic extraction of multiword expressions (MWEs). The method combines statistical information from a general-purpose corpus and texts from Wikipedia articles. We incorporate association measures via dimensions of data points to cluster MWEs and then compute the ranking score for each MWE based on the closest exemplar assigned to a...

This paper is about adopting the longest common subsequence (LCS) matching for Chinese information retrieval. We re-ranked the retrieved documents by a mixture of the original similarity score and the LCS score obtained by matching the document titles and the query. This LCS-based similarity score is also used in pseudo-relevance feedback in various ways (e.g., selecting terms and filtering doc...

In this study, both optimistic and pessimistic approaches of data envelopment analysis are applied to propose an equitable ranking method in fuzzy environments. To this end, we suppose that the sum of efficiency scores of all decision making units (DMUs) equals to unity. Using the worst-best and best-worst approaches, the minimum and maximum possible efficiency scores of each DMU are estimated ...

In this paper we present an approach to score aggregation for specialized search systems. In our work we focus on document ranking in scientific publication databases. We work with the collection of scientific publications of the CERN Document Server. This paper reports on work in progress and describes rank aggregation framework with score normalization. We present results that we obtained wit...