نتایج جستجو برای: document ranking
تعداد نتایج: 186064 فیلتر نتایج به سال:
The chosen retrieval engine was designed from the onset for retrieval of whole academic documents in XML [1]. A predecessor can be seen on BioMedNet and ChemWeb [4]. This engine, like that used in the IEEE digital library, returns relevance ranked lists of whole documents – the natural (citable) unit of information in an academic environment. From experience, information vendors are not interes...
In this paper, we describe a model of information retrieval system that is based on a document reranking method using document clusters. In the ®rst step, we retrieve documents based on the inverted®le method. Next, we analyze the retrieved documents using document clusters, and re-rank them. In this step, we use static clusters and dynamic cluster view. Consequently, we can produce clusters th...
This paper investigates the popular neural word embedding method Word2vec as a source of evidence in document ranking. In contrast to NLP applications of word2vec, which tend to use only the input embeddings, we retain both the input and the output embeddings, allowing us to calculate a different word similarity that may be more suitable for document ranking. We map the query words into the inp...
In this paper, we address the efficiency of implementing the tf x idf ranking strategy with inverted files. Two search methods are studied. The first one sorts postings lists of query terms based upon the list length. It is the traditional sorting method used in the upperbound search algorithm. The second one sorts postings lists based upon the maximum tf as well as the list length. We show tha...
Today, web plays a critical role in human life and also simplifies the same to a great extent. However, due to the towering increase in the number of web pages, the challenge of providing quality and relevant information to the users also needs to be addressed. Thus, search engines need to implement such algorithms which spans the pages as per user's interest and satisfaction and rank them...
The graph-based ranking algorithm has been recently exploited for multi-document summarization by making only use of the sentence-to-sentence relationships in the documents, under the assumption that all the sentences are indistinguishable. However, given a document set to be summarized, different documents are usually not equally important, and moreover, different sentences in a specific docum...
In this paper, we propose a co-ranking algorithm that trains listwise ranking functions using unlabeled data simultaneously with a small number of labeled data. The coranking algorithm is based on the co-training paradigm that is a very common scheme in the semi-supervised classification framework. First, we use two listwise ranking methods to construct base ranker and assistant ranker, respect...
Most existing learning to rank methods neglect query-sensitive information while producing functions to estimate the relevance of documents (i.e., all examples in the training data are treated indistinctly, no matter the query associated with them). This is counter-intuitive, since the relevance of a document depends on the query context (i.e., the same document may have different relevances, d...
In Information Retrieval (IR), whether implicitly or explicitly, queries and documents are often represented as vectors. However, it may be more beneficial to consider documents and/or queries as multidimensional objects. Our belief is this would allow building “truly” interactive IR systems, i.e., where interaction is fully incorporated in the IR framework. The probabilistic formalism of quant...
Learning a good ranking function plays a key role for many applications including the task of (multimedia) information retrieval. While there are a few rank learning methods available, most of them need to explicitly model the relations between every pair of relevant and irrelevant documents, and thus result in an expensive training process for large collections. The goal of this paper is to pr...
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