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What to be? - Electronic Career Guidance Based on Semantic Relatedness

Gurevych, Iryna ; Müller, Christof ; Zesch, Torsten (2007)
What to be? - Electronic Career Guidance Based on Semantic Relatedness.
Conference or Workshop Item, Bibliographie

Abstract

We present a study aimed at investigating the use of semantic information in a novel NLP application, Electronic Career Guidance (ECG), in German. ECG is formulated as an information retrieval (IR) task, whereby textual descriptions of professions (documents) are ranked for their relevance to natural language descriptions of a person’s professional interests (the topic). We compare the performance of two semantic IR models: (IR-1) utilizing semantic relatedness (SR) measures based on either wordnet or Wikipedia and a set of heuristics, and (IR-2) measuring the similarity between the topic and documents based on Explicit Semantic Analysis (ESA) (Gabrilovich and Markovitch, 2007). We evaluate the performance of SR measures intrinsically on the tasks of (T-1) computing SR, and (T-2) solving Reader’s Digest Word Power (RDWP) questions.

Item Type: Conference or Workshop Item
Erschienen: 2007
Creators: Gurevych, Iryna ; Müller, Christof ; Zesch, Torsten
Type of entry: Bibliographie
Title: What to be? - Electronic Career Guidance Based on Semantic Relatedness
Language: English
Date: 2007
Publisher: Association for Computational Linguistics
Book Title: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics
URL / URN: http://aclweb.org/anthology/P07-1130
Abstract:

We present a study aimed at investigating the use of semantic information in a novel NLP application, Electronic Career Guidance (ECG), in German. ECG is formulated as an information retrieval (IR) task, whereby textual descriptions of professions (documents) are ranked for their relevance to natural language descriptions of a person’s professional interests (the topic). We compare the performance of two semantic IR models: (IR-1) utilizing semantic relatedness (SR) measures based on either wordnet or Wikipedia and a set of heuristics, and (IR-2) measuring the similarity between the topic and documents based on Explicit Semantic Analysis (ESA) (Gabrilovich and Markovitch, 2007). We evaluate the performance of SR measures intrinsically on the tasks of (T-1) computing SR, and (T-2) solving Reader’s Digest Word Power (RDWP) questions.

Divisions: 20 Department of Computer Science
20 Department of Computer Science > Telecooperation
20 Department of Computer Science > Ubiquitous Knowledge Processing
Date Deposited: 31 Dec 2016 12:59
Last Modified: 24 Jan 2020 12:03
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