Gurevych, Iryna ; Müller, Christof ; Zesch, Torsten (2007)
What to be? - Electronic Career Guidance Based on Semantic Relatedness.
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (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.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2007 |
| Autor(en): | Gurevych, Iryna ; Müller, Christof ; Zesch, Torsten |
| Art des Eintrags: | Bibliographie |
| Titel: | What to be? - Electronic Career Guidance Based on Semantic Relatedness |
| Sprache: | Englisch |
| Publikationsjahr: | 2007 |
| Verlag: | Association for Computational Linguistics |
| Buchtitel: | Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics |
| URL / URN: | http://aclweb.org/anthology/P07-1130 |
| Kurzbeschreibung (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. |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Telekooperation 20 Fachbereich Informatik > Ubiquitäre Wissensverarbeitung |
| Hinterlegungsdatum: | 31 Dez 2016 12:59 |
| Letzte Änderung: | 24 Jan 2020 12:03 |
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