Papers by Marcus Klang

2 papers
Hedwig: A Named Entity Linker (2020.lrec-1)

Copied to clipboard

Challenge: Named entity linking is the task of identifying mentions of named things in text . e.g., "Barack Obama" or "New York" are examples of named entities .
Approach: They propose an end-to-end named entity linker that uses BILSTM models for mention detection and a PageRank algorithm for entity linking.
Outcome: The proposed named entity linker performs better than the previous generation, and is trilingually better.
Linking, Searching, and Visualizing Entities in Wikipedia (L18-1)

Copied to clipboard

Challenge: Existing systems to extract, index, search, and visualize entities in Wikipedia are not strings, but unique identifiers from Wikidata.
Approach: They propose a system to extract, index, search, and visualize entities in Wikipedia . they use a document model to store linguistic annotations and a string matching engine .
Outcome: The proposed system achieves CEAFm scores of 70.0 on English, 64.4 on Chinese, and 66.5 on Spanish.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations