Classifying entity mentions into a predefined set of categories achieves only a partial disambiguation of the names. This is further refined in the task of Named Entity Disambiguation, where names need to be linked to their actual denotations. In our research, we use Wikipedia as a repository of named entities and propose a ranking approach to disambiguation that exploits learned correlations between words from the name context and categories from the Wikipedia taxonomy.Therefore, the two methods share a common limitation: either through manual selection (Blaschke), or as a result of a greedy learning procedure (ELCS), they end up using only a subset of all possible anchored word sequences. ... Nevertheless, we can exploit dual learning algorithms that process examples only via computing their dot-products, such as in Support Vector Machines (SVMs ) (Vapnik, 1998anbsp;...
|Title||:||Learning for Information Extraction: From Named Entity Recognition and Disambiguation to Relation Extraction|
|Author||:||Razvan Constantin Bunescu|
|Publisher||:||ProQuest - 2007|