• Corpus ID: 11034290

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

@inproceedings{Gurevych2007WhatTB,
  title={What to be? - Electronic Career Guidance Based on Semantic Relatedness},
  author={Iryna Gurevych and Christof M{\"u}ller and Torsten Zesch},
  booktitle={ACL},
  year={2007}
}
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… 
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References

SHOWING 1-10 OF 28 REFERENCES
Exploring the Potential of Semantic Relatedness in Information Retrieval
TLDR
This paper explores the use of semantic relatedness in IR computed on the basis of GermaNet, a German wordnet, and presents several experiments on the German IR benchmarks GIRT'2005 (training set) and GIRT’2004 (test set) aimed at investigating the potential of semanticrelatedness inIR as opposed to bag-of-words models.
Computing Semantic Relatedness Using Wikipedia-based Explicit Semantic Analysis
TLDR
This work proposes Explicit Semantic Analysis (ESA), a novel method that represents the meaning of texts in a high-dimensional space of concepts derived from Wikipedia that results in substantial improvements in correlation of computed relatedness scores with human judgments.
Computing Semantic Relatedness in German with Revised Information Content Metrics
TLDR
An annotation study based on a revised definition of semantic relatedness beyond synonymy, an extension of Resnik’s (1995) procedure for computing information content of concepts for strongly inflected languages, an application of information content based metrics to compute semanticrelatedness of word senses defined in GermaNet and a new interpretation and normalization function for Jiang & Conrath's (1997) distance metric.
External Knowledge Sources for Question Answering
MIT CSAIL’s entries for the TREC Question Answering track (Voorhees, 2005) focused on incorporating external general-knowledge sources into the question answering process. We also explored the effect
Using the Structure of a Conceptual Network in Computing Semantic Relatedness
TLDR
The method relies solely on the structure of a conceptual network and eliminates the need for performing additional corpus analysis and can be easily applied to compute semantic relatedness based on alternative conceptual networks, e.g. in the domain of life sciences.
Query expansion using lexical-semantic relations
TLDR
Examination of the utility of lexical query expansion in the large, diverse TREC collection shows this query expansion technique makes little difference in retrieval effectiveness if the original queries are relatively complete descriptions of the information being sought even when the concepts to be expanded are selected by hand.
Evaluating WordNet-based Measures of Lexical Semantic Relatedness
TLDR
An information-content-based measure proposed by Jiang and Conrath is found superior to those proposed by Hirst and St-Onge, Leacock and Chodorow, Lin, and Resnik, and why distributional similarity is not an adequate proxy for lexical semantic relatedness.
Comparing Wikipedia and German Wordnet by Evaluating Semantic Relatedness on Multiple Datasets
TLDR
The combination of wordnets and Wikipedia to improve the performance of semantic relatedness measures is investigated, showing that their performance depends on the definition of relatedness that was underlying the construction of the evaluation dataset and the knowledge source used for computing semanticrelatedness.
Analyzing and accessing Wikipedia as a lexical semantic resource
TLDR
This work introduces a general purpose, high performance Java-based Wikipedia API that overcomes limitations and is available for research purposes at http://www.ukp.tu-darmstadt.
WordNet : an electronic lexical database
TLDR
The lexical database: nouns in WordNet, Katherine J. Miller a semantic network of English verbs, and applications of WordNet: building semantic concordances are presented.
...
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3
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