Add to ORKGAbstract. In this paper we present a textual retrieval system based on clustering and tiered indexes. Our system can be used for exact phrase matching and also for improved keyword search by employing term prox-imity weighting in the similarity measure. The document retrieval process is constructed in an efficient way, so that not all the documents in the database need to be compared against the searched query. 1
Document clustering, which is also refered to as text clustering, is a technique of unsupervised doc...
The process of clustering documents in a manner which produces accurate and compact clusters becomes...
Search system and method for retrieving relevant documents from a text data base collection comprise...
Abstract. This paper suggests the use of proximity measurement in combination with the Okapi probabi...
In addition to purely occurrence-based relevance models, term proximity has been frequently used to ...
Text search engines return a set of k documents ranked by similarity to a query. Typically, document...
Traditional index weighting approaches for information retrieval from texts depend on the term frequ...
Document Clustering is an issue of measuring similarity between documents and grouping similar docum...
P&al036International audienceIn this paper we propose a method for semantic text representation and ...
The information on the WWW is growing at an exponential rate; therefore, search engines are required...
We propose a method named WordRank to extract a few salient words from the target document and then ...
Abstract: Indexing used in text summarization has been an active area of current researches. Text su...
We propose a method named WordRank to extract a few salient words from the target document and then ...
We assess the suitability of word embeddings for practical information retrieval scenarios. Thus, we...
The purpose of text clustering in information retrieval is to discover groups of semantically relate...
Document clustering, which is also refered to as text clustering, is a technique of unsupervised doc...
The process of clustering documents in a manner which produces accurate and compact clusters becomes...
Search system and method for retrieving relevant documents from a text data base collection comprise...
Abstract. This paper suggests the use of proximity measurement in combination with the Okapi probabi...
In addition to purely occurrence-based relevance models, term proximity has been frequently used to ...
Text search engines return a set of k documents ranked by similarity to a query. Typically, document...
Traditional index weighting approaches for information retrieval from texts depend on the term frequ...
Document Clustering is an issue of measuring similarity between documents and grouping similar docum...
P&al036International audienceIn this paper we propose a method for semantic text representation and ...
The information on the WWW is growing at an exponential rate; therefore, search engines are required...
We propose a method named WordRank to extract a few salient words from the target document and then ...
Abstract: Indexing used in text summarization has been an active area of current researches. Text su...
We propose a method named WordRank to extract a few salient words from the target document and then ...
We assess the suitability of word embeddings for practical information retrieval scenarios. Thus, we...
The purpose of text clustering in information retrieval is to discover groups of semantically relate...
Document clustering, which is also refered to as text clustering, is a technique of unsupervised doc...
The process of clustering documents in a manner which produces accurate and compact clusters becomes...
Search system and method for retrieving relevant documents from a text data base collection comprise...