Publishing Partner: Cambridge University Press CUP Extra Publisher Login
amazon logo
More Info

New from Oxford University Press!


May I Quote You on That?

By Stephen Spector

A guide to English grammar and usage for the twenty-first century, pairing grammar rules with interesting and humorous quotations from American popular culture.

New from Cambridge University Press!


The Cambridge Handbook of Endangered Languages

Edited By Peter K. Austin and Julia Sallabank

This book "examines the reasons behind the dramatic loss of linguistic diversity, why it matters, and what can be done to document and support endangered languages."

Academic Paper

Title: Wisdom of crowds versus wisdom of linguists – measuring the semantic relatedness of words
Author: Torsten Zesch
Institution: Technische Universität Darmstadt
Author: Iryna Gurevych
Institution: Technische Universität Darmstadt
Linguistic Field: Computational Linguistics; Semantics; Text/Corpus Linguistics
Subject Language: English
Abstract: 'In this article, we present a comprehensive study aimed at computing semantic relatedness of word pairs. We analyze the performance of a large number of semantic relatedness measures proposed in the literature with respect to different experimental conditions, such as (i) the datasets employed, (ii) the language (English or German), (iii) the underlying knowledge source, and (iv) the evaluation task (computing scores of semantic relatedness, ranking word pairs, solving word choice problems). To our knowledge, this study is the first to systematically analyze semantic relatedness on a large number of datasets with different properties, while emphasizing the role of the knowledge source compiled either by the ‘wisdom of linguists’ (i.e., classical wordnets) or by the ‘wisdom of crowds’ (i.e., collaboratively constructed knowledge sources like Wikipedia).
The article discusses benefits and drawbacks of different approaches to evaluating semantic relatedness. We show that results should be interpreted carefully to evaluate particular aspects of semantic relatedness. For the first time, we employ a vector based measure of semantic relatedness, relying on a concept space built from documents, to the first paragraph of Wikipedia articles, to English WordNet glosses, and to GermaNet based pseudo glosses. Contrary to previous research (Strube and Ponzetto 2006; Gabrilovich and Markovitch 2007; Zesch et al. 2007), we find that ‘wisdom of crowds’ based resources are not superior to ‘wisdom of linguists’ based resources. We also find that using the first paragraph of a Wikipedia article as opposed to the whole article leads to better precision, but decreases recall. Finally, we present two systems that were developed to aid the experiments presented herein and are freely available for research purposes: (i) DEXTRACT, a software to semi-automatically construct corpus-driven semantic relatedness datasets, and (ii) JWPL, a Java-based high-performance Wikipedia Application Programming Interface (API) for building natural language processing (NLP) applications.


This article appears IN Natural Language Engineering Vol. 16, Issue 1, which you can READ on Cambridge's site or on LINGUIST .

Add a new paper
Return to Academic Papers main page
Return to Directory of Linguists main page