N12-1007 for the new task : cross-lingual entity clustering . 7.1 Intrinsic Evaluations Cross-lingual
N07-1043 method proves useful in a named entity clustering task . In future , we intend
N12-1007 knowledge , the cross-lingual entity clustering task is novel . However , there
J08-1001 humans do . Therefore , a simple entity clustering method is more suitable for automatic
D13-1159 and F1 score . For evaluating entity clustering results , we adopt B - cubed
D15-1182 the posterior distribution over entity clusterings . This distribution is a complex
D15-1182 coreference , while the final entity clustering includes more links implied through
N07-1043 named entities , we set up a named entity clustering task . We selected 50 person
N12-1007 for Arabic-English crosslingual entity clustering .8 Maxent Mention Similarity
D13-1159 evaluating entity extraction and entity clustering : Set EE . This set is employed
N07-1043 measure in a real-world named entity clustering task and measure its performance
N12-1007 Christopher Abstract Standard entity clustering systems commonly rely on mention
N07-1043 - measure of 0.78 ) in a named entity clustering task , proving the capability
D13-1159 with 0 = 0.1 for hierarchical entity clustering . 4 User Study In this section
D11-1142 extractors , but rely on document and entity clustering , which is too costly for Web-scale
D13-1159 automatically evaluate hierarchical entity clustering and select the best clustering
D13-1159 Section 3.2.2 ) , and hierarchical entity clustering ( Section 3.2.3 ) . 3.1.1 Entity
N12-1007 Distribution Unlimited ) . <title> Entity Clustering Across Languages </title> Nicholas
D13-1159 trees ( ETs ) " . 3 . Hierarchical entity clustering . In an ET , some entities are
N12-1007 Strassel , 2008 ) . &#8226; Named entity clustering : Assign semantic types to text
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