Paper: Collective Named Entity Disambiguation using Graph Ranking and Clique Partitioning Approaches

ACL ID C14-1147
Title Collective Named Entity Disambiguation using Graph Ranking and Clique Partitioning Approaches
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2014
Authors

Disambiguating named entities (NE) in running text to their correct interpretations in a specific knowledge base (KB) is an important problem in NLP. This paper presents two collective disam- biguation approaches using a graph representation where possible KB candidates for NE textual mentions are represented as nodes and the coherence relations between different NE candidates are represented by edges. Each node has a local confidence score and each edge has a weight. The first approach uses Page-Rank (PR) to rank all nodes and selects a candidate based on PR score combined with local confidence score. The second approach uses an adapted Clique Par- titioning technique to find the most weighted clique and expands this clique until all NE textual mentions are disambiguated. Experiments on 2...