The document discusses a multilingual strategy for information extraction using a system called PolyglotIE. The strategy involves defining language-independent rules in Annotation Query Language (AQL) that can be executed over multilingual text to extract semantic role labels. This allows information extraction to work immediately across languages with minimal effort. The rules are defined over universal proposition banks containing frames and roles that are shared across languages.

1. Multilingual Strategy
Alan Akbik, Laura Chiticariu, Marina Danilevsky, Yonas Kbrom, Yunyao Li, Huaiyu Zhu
IBM Research – Almaden, Yale University
Multilingual Information Extraction with PolyglotIE
Generating High Quality Proposition Banks for Multilingual Semantic Role Labeling. Alan Akbik, Laura Chiticariu, Marina Danilevsky, Yunyao Li, Shivakumar Vaithyanathan and Huaiyu Zhu. ACL 2015.
Towards Semi-Automatic Generation of Proposition Banks for Low-Resource Languages. Alan Akbik and Yunyao Li. EMNLP 2016.
1. Input Multilingual Text Data
2. Define AQL rule
3. Extract
Multilingual Semantic Role Labeling
How can we enable
Multilingual Information Extraction
at minimal effort?
• No language-specific adaptation
• IE immediately works across languages
Universal Proposition Banks
Version 1.0
Public release!
https://github.com/System-T/UniversalPropositions/
Declarative Information Extraction
• Similar to SQL, with SELECT, FROM
and WHERE statements
• The SELECT statement defines the
output columns of the extractor
• The WHERE statement specific
conditions of the extractor
Define language-independent AQL
rule
• Over SRL frames and roles from the
English Proposition Bank
• We use the same frames and roles
across all languages
Execute language-independent
AQL rule over multilingual text
IBM System-T: Declarative
Information Extraction
System