scholarly journals Intelligent Resume Retrieval Based on Lucence

2022 ◽  
pp. 29-35
Author(s):  
Jianping Du ◽  

With the development of Internet, the electronic resume has gradually replaced the paper one. It is the basic requirement of recruitment for enterprises to retrieve the talent information that fulfills the requirement quickly and without omission.Based on the framework of SpringBoot and Lucence full-text search engine, this paper implements a resume intelligent filtering algorithm, which improves the query speed of the system by establishing an index database. At the same time,the scoring function improves the accuracy of the filtering results, reduces the pressure of high concurrency of the database, improves the work efficiency of the Human Resources Department, and avoids the talent loss.

2012 ◽  
Vol 02 (04) ◽  
pp. 106-109 ◽  
Author(s):  
Rujia Gao ◽  
Danying Li ◽  
Wanlong Li ◽  
Yaze Dong

Author(s):  
Mary Holstege

To a search engine, indexes are specified by the content: the words, phrases, and characters that are actually present tell the search engine what inverted indexes to create. Other external knowledge can be applied add to this inventory of indexes. For example, knowledge of the document language can lead to indexes for word stems or decompounding. These can unify different content into the same index or split the same content into multiple indexes. That is, different words manifest in the content can be unified under a single search key, and the same word can have multiple manifestations under different search keys. Turning this around, the indexes represent the retrievable information content in the document. Full text search is not an either/or yes/no system, but one of relative fit (scoring). Precision balances against recall, mediated by scoring. The search engine perspective offers a different way to think about markup: As a specification of the retrievable information content of the document. As something that can, with additional information, unify different markup or provide multiple distinct views of the same markup. As something that can be present to greater or lesser degrees, with a goodness of match (scoring). As a specification that can be adjusted to balance precision and recall. What does this search engine perspective on markup mean, concretely? Can we use it to reframe some persistent conundrums, such as vocabulary resolution and overlap? Let's see.


Author(s):  
Cornelia Gyorodi ◽  
Robert Gyorodi ◽  
George Pecherle ◽  
George Mihai Cornea

In this article we will try to explain how we can create a search engine using the powerful MySQL full-text search. The ever increasing demands of the web requires cheap and elaborate search options. One of the most important issues for a search engine is to have the capacity to order its results set as relevance and provide the user with suggestions in the case of a spelling mistake or a small result set. In order to fulfill this request we thought about using the powerful MySQL full-text search. This option is suitable for small to medium scale websites. In order to provide sound like capabilities, a second table containing a bag of words from the main table together with the corresponding metaphone is created. When a suggestion is needed, this table is interrogated for the metaphone of the searched word and the result set is computed resulting a suggestion.


Author(s):  
Pavel Šimek ◽  
Jiří Vaněk ◽  
Jan Jarolímek

The majority of Internet users use the global network to search for different information using fulltext search engines such as Google, Yahoo!, or Seznam. The web presentation operators are trying, with the help of different optimization techniques, to get to the top places in the results of fulltext search engines. Right there is a great importance of Search Engine Optimization and Search Engine Marketing, because normal users usually try links only on the first few pages of the fulltext search engines results on certain keywords and in catalogs they use primarily hierarchically higher placed links in each category. Key to success is the application of optimization methods which deal with the issue of keywords, structure and quality of content, domain names, individual sites and quantity and reliability of backward links. The process is demanding, long-lasting and without a guaranteed outcome. A website operator without advanced analytical tools do not identify the contribution of individual documents from which the entire web site consists. If the web presentation operators want to have an overview of their documents and web site in global, it is appropriate to quantify these positions in a specific way, depending on specific key words. For this purpose serves the quantification of competitive value of documents, which consequently sets global competitive value of a web site. Quantification of competitive values is performed on a specific full-text search engine. For each full-text search engine can be and often are, different results. According to published reports of ClickZ agency or Market Share is according to the number of searches by English-speaking users most widely used Google search engine, which has a market share of more than 80%. The whole procedure of quantification of competitive values is common, however, the initial step which is the analysis of keywords depends on a choice of the fulltext search engine.


2011 ◽  
Vol 21 (2) ◽  
pp. 191-196
Author(s):  
Tatsuma KAWANAKA ◽  
WATAGAMI Yukiharu ◽  
Takehiko MURAKAWA ◽  
Masaru NAKAGAWA

Sign in / Sign up

Export Citation Format

Share Document