We propose a method named WordRank to extract a few salient words from the target document and then use these words to retrieve similar documents based on popular retrieval functions. The set of extracted words is a concise and topic-oriented representation of the target document and reduces the ambiguous and noisy information in the document, so as to improve the retrieval performance. Experiments and results demonstrate the high effectiveness of the proposed approach. ? Springer-Verlag Berlin Heidelberg 2005.EI
The focus of this thesis is comparison of analysis of text-document similarity using clustering algo...
With large number of documents on the web, there is a increasing need to be able to retrieve the bes...
A lexical signature of a web page consists of several key words carefully chosen from the web page a...
We propose a method named WordRank to extract a few salient words from the target document and then ...
Abstract. In this paper we present a textual retrieval system based on clustering and tiered indexes...
We assess the suitability of word embeddings for practical information retrieval scenarios. Thus, we...
Text search engines return a set of k documents ranked by similarity to a query. Typically, document...
An automatic method for text categorizing and ranking search engine's results by semantic similarity...
Particularly, information retrieval resultsas documents are typically too extensive.Consequently, a ...
Abstract — As the volume of information is in internet is increasing staggeringly therefore it is re...
Word similarity is a semantic measure that evaluates the similarity of words. The goal of the master...
Document similarity search is to find documents similar to a given query document and return a ranke...
Abstract: Indexing used in text summarization has been an active area of current researches. Text su...
Abstract. Measuring the similarity between documents and queries has been extensively studied in inf...
Document similarity search aims to find documents similar to a query document in a text corpus and r...
The focus of this thesis is comparison of analysis of text-document similarity using clustering algo...
With large number of documents on the web, there is a increasing need to be able to retrieve the bes...
A lexical signature of a web page consists of several key words carefully chosen from the web page a...
We propose a method named WordRank to extract a few salient words from the target document and then ...
Abstract. In this paper we present a textual retrieval system based on clustering and tiered indexes...
We assess the suitability of word embeddings for practical information retrieval scenarios. Thus, we...
Text search engines return a set of k documents ranked by similarity to a query. Typically, document...
An automatic method for text categorizing and ranking search engine's results by semantic similarity...
Particularly, information retrieval resultsas documents are typically too extensive.Consequently, a ...
Abstract — As the volume of information is in internet is increasing staggeringly therefore it is re...
Word similarity is a semantic measure that evaluates the similarity of words. The goal of the master...
Document similarity search is to find documents similar to a given query document and return a ranke...
Abstract: Indexing used in text summarization has been an active area of current researches. Text su...
Abstract. Measuring the similarity between documents and queries has been extensively studied in inf...
Document similarity search aims to find documents similar to a query document in a text corpus and r...
The focus of this thesis is comparison of analysis of text-document similarity using clustering algo...
With large number of documents on the web, there is a increasing need to be able to retrieve the bes...
A lexical signature of a web page consists of several key words carefully chosen from the web page a...