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A Multilingual Entity Linker Using PageRank And Semantic Graphs.pdf

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Preview of A Multilingual Entity Linker Using PageRank and Semantic Graphs
🔗 Source: ep.liu.se
📊 Size: 762 KB
👤 Author: Anton Sodergren ; Pierre Nugues
⬇️ Downloads: 82

Summary

Problem: Entity linking aims to map ambiguous mentions in text (e.g., "Michael Jackson") to specific entities (e.g., the singer) using knowledge from sources like Wikipedia and Wikidata. This is crucial for applications like search engines, question answering, and dialogue agents.

Solution: HERD leverages:

Wikipedia Links: As a source of entity-mention pairs and their frequencies.
 Wikidata: A language-agnostic database providing unique identifiers (Q-numbers) for entities and structured information like dates.
PageRank Algorithm: To rank potential entities based on the strength of links connecting them to mentions.
 Feature Vectors: Derived from Wikipedia categories, stop words, and dictionary words, to further distinguish between entity candidates.

Key Features:

Multilingual: Supports English, French, and Swedish.
 End-to-end: Combines named entity recognition (NER) and linking in a single pipeline.
Competitive Performance: Achieved an F1-score of 0.746 on the ERD’14 development set.

Evaluation:

The authors evaluated HERD using:

The ERD’14 challenge dataset (long documents and search queries)
* The CoNLL-2003 dataset enriched with Wikidata links

Results:

HERD demonstrates strong performance, highlighting its potential for real-world applications requiring accurate entity linking across diverse text types.

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Description

HERD, a multilingual named entity recognizer and linker, leverages Wikipedia links and Wikidata for entity resolution. It combines string matching, rules, PageRank, and category-based feature vectors to achieve a high F1-score in evaluations.

Technical Information

  • File Format: PDF
  • File Size: 762 KB
  • Pages: 9
  • Language: EN
  • Author: Anton Sodergren ; Pierre Nugues
  • Total Downloads: 82
  • Last Updated: 2 hours ago

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