نتایج جستجو برای: ranking keywords
تعداد نتایج: 2006051 فیلتر نتایج به سال:
The paper contains a review of the literature in terms of the critical analysis of methodologies of university ranking systems. Furthermore, the initiatives supported by the European Commission (U-Map, U-Multirank) and CHE Ranking are described. Special attention is paid to the tendencies in the development of ranking systems. According to the author, the ranking organizations should abandon th...
The purpose of this study is to utilize a new method for ranking extreme efficient decision making units (DMUs) based upon the omission of these efficient DMUs from reference set of inefficient and non-extreme efficient DMUs in data envelopment analysis (DEA) models with constant and variable returns to scale. In this method, an L2- norm is used and it is believed that it doesn't have any e...
As social media and e-commerce on the Internet continue to grow, opinions have become one of the most important sources of information for users to base their future decisions on. Unfortunately, the large quantities of opinions make it difficult for an individual to comprehend and evaluate them all in a reasonable amount of time. The users have to read a large number of opinions of different en...
A hierarchical framework is proposed to address the issues of modeling different type of words in keyword spotting (KWS). Keyword models are built at various levels according to the availability of training set resources for each individual word. The proposed approach improves the performance of KWS even when no training speech is available for the keywords. It also suggests an easier way to co...
boolean byte class double else extends final finally float if implements int interface long native new private public return short static super synchronized this throw throws transient try void volatile while ( ) [ ] { } ; , . = Figure 1: Java keywords.
In this paper we describe our effort on TREC Contextual Suggestion Track. We present a recommendation system that built upon ElasticSearch along with a machine learning re-ranking model. We utilize real world users’ opinion as well as other information to build user profiles. With profile information, we then construct customized ElasticSearch queries to search on various fields. After that, a ...
For Document Search Task, we generally applied BM25 formula separately on different fields of HTML pages: Title, Anchor, H1, H2, Keywords, and Extracted Body. Various Static Ranking methods are also exploited. Scores are combined together using linear combination. Among all the techniques we have embedded in our system, our highlight is the static ranking approaches. Beside this, some data prep...
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