The semantic enrichment aims at extending the knowledge about the entities that lie in the context. Document enriching is today a fundamental technique to improve the quality of several text analysis tasks, including Web search. In this work, we present a mechanism that functions as a contextualizing tool that helps user understand more about the entities through different dimensions, i.e., cross-languages, global authority, distinctiveness and public attractiveness.

Information retrieval, summarization, and online advertising rely on identifying the most important words and phrases in web documents. While traditional techniques treat documents as collections of keywords, many NLP systems are shifting toward understanding documents in terms of entities. We revisit the task of entity salience: assigning a relevance score to each entity in a document.

This is implemented as a UIMA component.

The metadata are listed below:

• authority (PageRank) of co-occurance graphs
• authority (PageRank) of Wikipedia link graph
• popularity (Pageview)
• Entity-based IDF (Inverse Document Frequency)