JOURNAL ARTICLE

Exploiting information extraction annotations for document retrieval in distillation tasks

Abstract

Information distillation aims to extract relevant pieces of information related to a given query from massive, possibly multilingual, audio and textual document sources. In this paper, we present our approach for using information extraction annotations to augment document retrieval for distillation. We take advantage of the fact that some of the distillation queries can be associated with annotation elements introduced for the NIST Automatic Content Extraction (ACE) task. We experimentally show that using the ACE events to constrain the document set returned by an information retrieval engine significantly improves the precision at various recall rates for two different query templates. Index Terms: information distillation, information retrieval, information extraction, document retrieval

Keywords:
Computer science Information retrieval Distillation NIST Information extraction Set (abstract data type) Precision and recall Annotation Document retrieval Task (project management) Artificial intelligence Natural language processing

Metrics

7
Cited By
1.94
FWCI (Field Weighted Citation Impact)
9
Refs
0.87
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Text and Document Classification Technologies
Physical Sciences →  Computer Science →  Artificial Intelligence
Topic Modeling
Physical Sciences →  Computer Science →  Artificial Intelligence
Natural Language Processing Techniques
Physical Sciences →  Computer Science →  Artificial Intelligence

Related Documents

© 2026 ScienceGate Book Chapters — All rights reserved.