Enhanced E-recruitment using Semantic Retrieval of Modeled Serialized Documents

Abstract

Retrieval in existing e-recruitment system is on exact match between applicants" stored profiles and inquirer"s request. These profiles are captured through online forms whose fields are tailored by recruiters and hence, applicants sometimes do not have privilege to present details of their worth that are not captured by the tailored fields thereby, leading to their disqualification. This paper presents a 3-tier system that models serialized documents of the applicants" worth and they are analyzed using document retrieval and natural language processing techniques for a human-like assessment. Its presentation tier was developed using java server pages and middle tier functionalities using web service technology. The data tier models ré sumé s that have been tokenized and tagged using Brill Algorithm with my sequel. Within the middle tier, indexing was achieved using an inverted index whose terms are noun phrases extracted from ré sumé s that have been tokenized and tagged using Brill Algorithm.

Smart job searching system based on information retrieval techniques and similarity of fuzzy parameterized sets

“…AJS then offers a list of jobs combined with links to their correspondent employers to the Jobseeker. A similar study can be found in the work of Owoseni et al, [15] where they introduced a 3-tier electronic recruitment system which models serialized information of the applicants such as their income and social circumstances. The documents are analyzed using document retrieval and natural language processing in order to examine documents and applications just like humans would do.…”

A New Approach to Information Extraction in User-Centric E-Recruitment Systems

“…Another system KELVIN [30] extracts entities and relations from large text collections. The core features of KELVIN are (1) cross-document entity co-references, (2) inter-document co-reference chains, (3) a slot value consolidator for entities, (4) the application of inference rules to expand the number of asserted facts and (5) a set of analysis and browsing tools supporting development.…”

“…It utilizes a hybrid approach to combine existing Natural Language Processing (NLP) techniques with the new form of context-driven extraction techniques for extracting the layout, structure and content information of a job description. Owoseni et al [2] built a 3-tier technique using a semantic model. This technique performs analysis using document retrieval and natural language processing techniques for a human-like assessment.…”

“…The matching process is complex and needs to understand the context (i.e., domain-specific information) of the text to resolve semantic heterogeneity in matchmaking. Existing e-recruitment systems [2,3] do not extract domain-specific information, such as 'mobile application' in the above example from the job requirement text to match with the candidate query of 'Android Development'. These domain-specific entities are contextually associated with each other.…”