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HITS’ Cross-lingual Entity Linking
System at TAC 2011
One Model for All Languages
Angela Fahrni, Michael Strube, Vivi Nastase
Heidelberg Institute for Theoretical Studies gGmbHHeidelberg, Germany
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Strategies for Cross-lingualEntity Disambiguation
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Strategies for Cross-lingualEntity Disambiguation
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Strategies for Cross-lingualEntity Disambiguation
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Strategies for Cross-lingualEntity Disambiguation
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Strategies for Cross-lingualEntity Disambiguation
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Knowledge base
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Knowledge base
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Mapping between the Englishand Chinese Wikipedia Versions
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Mapping between the Englishand Chinese Wikipedia Versions
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Mapping between the Englishand Chinese Wikipedia Versions
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Mapping between the Englishand Chinese Wikipedia Versions
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Coverage after Lexicon Lookup
Training SetCoverage Average Ambiguity
EN 0.928 16.92ZH 0.867 5.56ZH_Lex 0.930 22.86
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Different Techniques for DifferentLanguages
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Different Models for DifferentLanguages
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One Model for All Languages
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Context DisambiguationApproach
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Context DisambiguationApproach
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Context DisambiguationApproach
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Context DisambiguationApproach
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Context DisambiguationApproach
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Context DisambiguationApproach
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Context DisambiguationApproach
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Context DisambiguationApproach
• Training of a supervised model (SVM) using English traininginstances
• Training instances are derived from the internal hyperlinks inWikipedia
• Positive instances: linked Wikipedia articles• Negative instances: other Wikipedia articles the terms can refer to
• Model is used for English and Chinese
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Entity Disambiguation
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Entity Disambiguation Approach
• Training instances are derived from the training data provided byTAC
• Positive instances: correct entity for the query terms• Negative instances: other candidate entities for the query terms
• Training of a supervised model (SVM) based on• English and Chinese training instances• Chinese training instances• English instances
• Application of the models to both languages
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One-Model-for-All-Languages:Features
• Abstraction from particular languages, i.e. no lexical features
• Relatedness measures
• Concept-based features
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One-Model-for-All-Languages:Features
• Prior probability
• Distance between term and name of the respective Wikipedia article
• Context fit on conceptual level• Vector-based features based on concepts, categories, lists, portals• Average, maximum and minimum derived from pairwise calculated
relatedness measures• Relatedness measures based on outgoing links, incoming links,
categories
• Context fit on token level: Cosine similarity based on nouns, verbs,adjectives between:
• Whole text and respective Wikipedia article• Surrounding context and local context of hyperlinks pointing to the
respective page in Wikipedia
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Results Micro-AverageDifferent Training Approaches
Micro-AverageHITS_Trained_EN_ZH 0.785
HITS_Trained_SEP 0.789HITS_Trained_ZH 0.786
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Language-independentConcept-based Representation
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Language-independentConcept-based Representation
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Graph
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Clustering
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Learning the Edge Weights
• Binary classifier (SMV) with the classes:• In same cluster• Not in the same cluster
• Confidence values as edge weights
• Features: Cosine similarities between local contexts and whole textsbased on:
• Identified concepts• Identified concepts extended by incoming and outgoing links• Categories associated with the concepts• Lists associated with the concepts• Portals associated with the concepts
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Shortcomings of Graph-basedClustering Approach
• Construction of the graph is inefficient• Pairwise comparisons using different similarity measures
• Few data to optimize the parameters
• Temporary solution: String match heuristic
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Results
Micro-Average Precision (B3) Recall (B3) F1 (B3)Best System 0.788Median 0.675HITS 0.785 0.700 0.763 0.730
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Future Work
• Experiments with other languages• We participated in the cross-lingual link discovery task organized by
NTCIR: good results for all subtasks (EN-to-Chinese, EN-to-Korean,EN-to-Japanese)
• Efficient way to cluster terms: Circumventing pairwise comparisons
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