Exploring Resources for Lexical Chaining: A Comparison of Automated Semantic Relatedness Measures and Human Judgements
Title: Exploring Resources for Lexical Chaining: A Comparison of Automated Semantic Relatedness Measures and Human Judgements
Authors: Irene Cramer, Tonio Wandmacher, and Ulli Waltinger
Pub/Conf: Modeling, Learning and Processing of Text Technological Data Structures. Mehler A, Henning Lobin and Harald Lüngen and K-UK, Storrer A, Witt A (Eds); Studies in Computational Intelligence, Berlin/New York: Springer
Abstract:
In the past decade various semantic relatedness, similarity, and distance measures have been proposed which play a crucial role in many NLP-applications. Researchers compete for better algorithms (and resources to base the algorithms on), and often only few percentage points seem to suffice in order to prove a new measure (or resource) more accurate than an older one. However, it is still unclear which of them performs best under what conditions. In this work we therefore present a study comparing various relatedness measures. We evaluate them on the basis of a human judgment experiment and also examine several practical issues, such as run time and coverage. We show that the performance of all measures – as compared to human estimates – is still mediocre and argue that the definition of a shared task might bring us considerably closer to results of high quality
BibTeX:
@incollection{DBLP:series/sci/CramerWW12,
author = {Irene M. Cramer and
Tonio Wandmacher and
Ulli Waltinger},
title = {Exploring Resources for Lexical Chaining: A Comparison of
Automated Semantic Relatedness Measures and Human Judgments},
booktitle = {Modeling, Learning, and Processing of Text Technological
Data Structures},
year = {2012},
pages = {377-396},
ee = {http://dx.doi.org/10.1007/978-3-642-22613-7_18},
crossref = {DBLP:series/sci/2012-370},
bibsource = {DBLP, http://dblp.uni-trier.de}
}
