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. © 2011 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Cramer, I., Wandmacher, T., & Waltinger, U. (2011). Exploring resources for lexical chaining: A comparison of automated semantic relatedness measures and human judgments. Studies in Computational Intelligence, 370, 377–396. https://doi.org/10.1007/978-3-642-22613-7_18
Mendeley helps you to discover research relevant for your work.