Torsten Zesch

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Torsten Zesch is an author.


Only those publications related to wikis are shown here.
Title Keyword(s) Published in Language DateThis property is a special property in this wiki. Abstract R C
Detecting malapropisms using measures of contextual fitness Contextual fitness
Wikipedia revision history
TAL Traitement Automatique des Langues English 2012 While detecting simple language errors (e.g. misspellings, number agreement, etc.) is nowadays standard functionality in all but the simplest text-editors, other more complicated language errors might go unnoticed. A difficult case are errors that come in the disguise of a valid word that fits syntactically into the sentence. We use the Wikipedia revision history to extract a dataset with such errors in their context. We show that the new dataset provides a more realistic picture of the performance of contextual fitness measures. The achieved error detection quality is generally sufficient for competent language users who are willing to accept a certain level of false alarms, but might be problematic for non-native writers who accept all suggestions made by the systems. We make the full experimental framework publicly available which will allow other scientists to reproduce our experiments and to conduct follow-up experiments. 0 0
Combining heterogeneous knowledge resources for improved distributional semantic models Lecture Notes in Computer Science English 2011 The Explicit Semantic Analysis (ESA) model based on term cooccurrences in Wikipedia has been regarded as state-of-the-art semantic relatedness measure in the recent years. We provide an analysis of the important parameters of ESA using datasets in five different languages. Additionally, we propose the use of ESA with multiple lexical semantic resources thus exploiting multiple evidence of term cooccurrence to improve over the Wikipedia-based measure. Exploiting the improved robustness and coverage of the proposed combination, we report improved performance over single resources in word semantic relatedness, solving word choice problems, classification of semantic relations between nominals, and text similarity. 0 0
Wikipedia revision toolkit: Efficiently accessing Wikipedia's edit history ACL HLT 2011 - 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Proceedings of Student Session English 2011 We present an open-source toolkit which allows (i) to reconstruct past states of Wikipedia, and (ii) to efficiently access the edit history of Wikipedia articles. Reconstructing past states of Wikipedia is a prerequisite for reproducing previous experimental work based on Wikipedia. Beyond that, the edit history of Wikipedia articles has been shown to be a valuable knowledge source for NLP, but access is severely impeded by the lack of efficient tools for managing the huge amount of provided data. By using a dedicated storage format, our toolkit massively decreases the data volume to less than 2% of the original size, and at the same time provides an easy-to-use interface to access the revision data. The language-independent design allows to process any language represented in Wikipedia. We expect this work to consolidate NLP research using Wikipedia in general, and to foster research making use of the knowledge encoded in Wikipedia's edit history. 0 0
Wikipedia revision toolkit: efficiently accessing Wikipedia's edit history HLT English 2011 0 0
Wikulu: An extensible architecture for integrating natural language processing techniques with wikis ACL HLT 2011 - 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Proceedings of Student Session English 2011 We present Wikulu 1, a system focusing on supporting wiki users with their everyday tasks by means of an intelligent interface. Wikulu is implemented as an extensible architecture which transparently integrates natural language processing (NLP) techniques with wikis. It is designed to be deployed with any wiki platform, and the current prototype integrates a wide range of NLP algorithms such as keyphrase extraction, link discovery, text segmentation, summarization, or text similarity. Additionally, we show how Wikulu can be applied for visually analyzing the results of NLP algorithms, educational purposes, and enabling semantic wikis. 0 0
Wisdom of crowds versus wisdom of linguists - Measuring the semantic relatedness of words Natural Language Engineering English 2010 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 available1 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. Copyright 0 0
An architecture to support intelligent user interfaces for Wikis by means of Natural Language Processing Wiki
Content organization
Natural Language Processing
User interaction
WikiSym English 2009 0 0
Wisdom of crowds versus wisdom of linguists “ measuring the semantic relatedness of words Natural Language Engineering 2009 0 0
Extracting Lexical Semantic Knowledge from Wikipedia and Wiktionary Wikipedia API
Ubiquitous Knowledge Processing Lab#Wiktionary_API
LREC'08 2008 Recently, collaboratively constructed resources such as Wikipedia and Wiktionary have been discovered as valuable lexical semantic knowledge bases with a high potential in diverse Natural Language Processing (NLP) tasks. Collaborative knowledge bases however significantly differ from traditional linguistic knowledge bases in various respects, and this constitutes both an asset and an impediment for research in NLP. This paper addresses one such major impediment, namely the lack of suitable programmatic access mechanisms to the knowledge stored in these large semantic knowledge bases. We present two application programming interfaces for Wikipedia and Wiktionary which are especially designed for mining the rich lexical semantic information dispersed in the knowledge bases, and provide efficient and structured access to the available knowledge. As we believe them to be of general interest to the NLP community, we have made them freely available for research purposes. 0 1
Graph-theoretic analysis of collaborative knowledge bases in Natural Language Processing CEUR Workshop Proceedings English 2008 We present a graph-theoretic analysis of the topological structures underlying the collaborative knowledge bases Wikipedia and Wiktionary, which are promising uprising resources in Natural Language Processing. We contrastively compare them to a conventional linguistic knowledge base, and address the issue of how these Social Web knowledge repositories can be best exploited within the Social-Semantic Web. 0 0
Using wiktionary for computing semantic relatedness Proceedings of the National Conference on Artificial Intelligence English 2008 We introduce Wiktionary as an emerging lexical semantic resource that can be used as a substitute for expert-made resources in AI applications. We evaluate Wiktionary on the pervasive task of computing semantic relatedness for English and German by means of correlation with human rankings and solving word choice problems. For the first time, we apply a concept vector based measure to a set of different concept representations like Wiktionary pseudo glosses, the first paragraph of Wikipedia articles, English WordNet glosses, and GermaNet pseudo glosses. We show that: (i) Wiktionary is the best lexical semantic resource in the ranking task and performs comparably to other resources in the word choice task, and (ii) the concept vector based approach yields the best results on all datasets in both evaluations. Copyright © 2008, Association for the Advancement of Artificial Intelligence ( All rights reserved. 0 1
Analyzing and Accessing Wikipedia as a Lexical Semantic Resource. Api Biannual Conference of the Society for Computational Linguistics and Language Technology pp. 213-221 2007 We analyze Wikipedia as a lexical semantic resource and compare it with conventional resources, such as dictionaries, thesauri, semantic wordnets, etc. Different parts of Wikipedia record different aspects of these resources. We show that Wikipedia contains a vast amount of knowledge about, e.g., named entities, domain specific terms, and rare word senses. If Wikipedia is to be used as a lexical semantic resource in large-scale NLP tasks, efficient programmatic access to the knowledge therein is required. We review existing access mechanisms and show that they are limited with respect to performance and the provided access functions. Therefore, we introduce a general purpose, high performance Java-based Wikipedia API that overcomes these limitations. 0 0
Comparing Wikipedia and German Wordnet by Evaluating Semantic Relatedness on Multiple Datasets. Wordnet Proceedings of Human Language Technologies: The Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT) 2007 We evaluate semantic relatedness measures on different German datasets showing that their performance depends on: (i) the definition of relatedness that was underlying the construction of the evaluation dataset, and (ii) the knowledge source used for computing semantic relatedness. We analyze how the underlying knowledge source influences the performance of a measure. Finally, we investigate the combination of wordnets and Wikipedia to improve the performance of semantic relatedness measures. 0 0
What to be? - Electronic Career Guidance based on semantic relatedness ACL 2007 - Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics English 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 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. 0 0