A Random Graph Walk based Approach to Computing Semantic Relatedness Using Knowledge from Wikipedia Presenter: Ziqi Zhang OAK Research Group, Department of Computer Science, University of Sheffield Authors: Ziqi Zhang, Anna Lisa Gentile, Lei Xia, José Iria, Sam Chapman
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A Random Graph Walk based Approach to Computing Semantic Relatedness Using Knowledge from Wikipedia Presenter: Ziqi Zhang OAK Research Group, Department.
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A Random Graph Walk based Approach to Computing Semantic
Relatedness Using Knowledge from Wikipedia
Presenter: Ziqi ZhangOAK Research Group, Department of Computer Science,
University of SheffieldAuthors: Ziqi Zhang, Anna Lisa Gentile, Lei Xia, José Iria,
Sam Chapman
• Introduction to semantic relatedness
• Motivation to this research
• Methodology: random walk, Wikipedia, semantic relatedness
• Experiment and Evaluation: computing semantic relatedness, semantic relatedness for named entity disambiguation
In this presentation…In this presentation…
• Semantic relatedness (SR) measures how much words or concepts are related by encompassing all kinds of relations between them
• The experiments are designed to achieve three objectives– Analyse the importance of each proposed feature – Evaluate effectiveness of the random walk method for
computing semantic relatedness– Evaluate the usefulness of the method for solving other NLP
problems – Named Entity Disambiguation (NED)
Feature AnalysisFeature Analysis> Experiment
• Simulated Annealing optimisation (Nie et al., 2005) method is used to perform the analysis, in which– 200 pair of words from WordSim353 is used– To begin with, we treat each feature equally by assigning same
weights (weight model)– Compute SR using the weight model, and evaluate against the
gold standard– Hundreds of iterations are run, in each turn, different weight
model is generated randomly– Manually analysing the weight model that contribute to the
highest performance achieved on this dataset, eliminating least important features or combining them into other features that are semantically similar
Evaluating Computation of SREvaluating Computation of SR> Experiment
• Three datasets are chosen: different set of 153 pairs of words from WordSim353; 65 pairs from Rubenstein &Goodenough (1965), RG65; 30 pairs from Miller & Charles (1991), MC30
• Compared against: a collection of WordNet-based algorithms and other state-of-the-art methods for SR
WordSim353-153
RG65 MC30 WordSim353 -200 feature analysis
Ours 0.71 0.76 0.71 0.46
Strube & Ponzetto (2006) 0.55 0.69 0.67 /
Zesch et al. ESA (2008) 0.62 / / 0.31
Zesch et al. Wiki (2008) 0.7 0.76 0.68 0.5
Zesch et al. Wiktionary (2008) 0.7 0.84 0.84 0.6
Best of WordNet 0.39 0.79 0.81 0.23
Evaluating Usefulness of SR for NEDEvaluating Usefulness of SR for NED> Experiment
• The NED method in a nutshell (Details: Gentile et al., 2009)• Identify surfaces of NEs that occur in a text passage and
that are defined by Wikipedia, retrieve corresponding sense pages
• Computing SR of each pair of their underlying senses • The sense of a surface is determined collectively by the
senses of other surfaces found in the text (contexts)• Three functions are defined to capture this collective
context
Evaluating Usefulness of SR for NEDEvaluating Usefulness of SR for NED> Experiment
• Dataset: 20 news stories by Cucerzan (2007), each story contains 10 – 50 NEs
Accuracy
Our best 91.5
Our baseline 68.7
Cucerzan baseline 51.7
Curcerzan best 91.4
ConclusionConclusion• Computing SR isn’t an easy task
• Different structural and content information in Wikipedia all contribute to the task, but in different weights
• Combining these different features in a uniform measure can improve performance
• Can we use simpler similarity functions to obtain same results?
• Can we integrate different lexical resources?• How to compute relatedness/similarity of longer text
passages?
In future
Thank you!Thank you!
• Cucerzan, S. (2007). Large-Scale Named Entity Disambiguation Based on Wikipedia Data. In EMNLP’07• Finkelstein, L., Gabrilovich, E., Matias, Y., Rivlin, E., Solan, Z., Wolfman, G., and Ruppin, E. (2002). Placing search
in context: the concept revisited. In ACM Transactions on Information Systems, 20 (1), pp. 116 – 131 • Gabrilovich, E., Markovitch, S. (2007). Computing semantic relatedness using Wikipedia-based explicit
semantic analysis. In Proceedings of IJCAI’07, pp. 1606-1611 • Gentile, A., Zhang, Z., Xia, L., Iria, J. (2009). Graph-based semantic relatedness for named entity
disambiguation. In S3T• Leacock, C., Chodorow, M. (1998). Combining local context and WordNet similarity for word sense
identification. In C. Fellbaum (Ed.), WordNet. An Electronic Lexical Database, Chp. 11, pp. 265-283.• Miller, G., Charles, W. (1991). Contextual correlates of semantic similarity. In Language and Cognitive
Processes, 6(1): 1-28• Nie, Z., Zhang, Y., Wen, J., Ma, W. (2005). Object-level ranking: bringing order to web objects. In Proceedings of
WWW’05• Resnik, P. (1995). Using information content to evaluate semantic similarity in a taxonomy. In Proceedings of
IJCAI-95, pp. 448-453 • Rubenstein, H., Goodenough, J. (1965). Contextual correlates of synonymy. In Communications of the ACM,
8(10):627-633 • Strube, M., Ponzetto, S. (2006). WikiRelate! Computing semantic relatedness using Wikipedia. In AAAI’06• Zesch, T., Müller, C., Gurevych, I. (2008). Using Wiktionary for computing semantic relatedness. In Proceedings