Summary of the paper "Towards Better Text Understanding and Retrieval through Kernel Entity Salience Modeling", presented at SIGIR 2018. Date: October 17, 2018 Venue: London, UK. Reading group Please cite, link to or credit this presentation when using it or part of it in your work.

1. Reading Group
17.10.2018
Towards Better Text Understanding
and Retrieval through Kernel Entity
Salience Modeling
Xiong, Liu, Callan, and Liu
SIGIR 2018

2. Motivation
• Interest in knowing how salient (important and central)
a term (word, entity) is in a document
• Word frequency largely exploited for document
retrieval
• But frequency is not necessarily same as salience
• Entity salience is still a young task
• Effectiveness of salience for ad hoc search is not
explored yet

3. Main Messages
• They represent an entity combining the textual
information and semantics from knowledge base
• They obtain a better modeling of salience, beyond
frequency, by entity interactions
• They can improve web document search with
salience, generalizing from news corpus

4. Knowledge Enriched
Embedding
• Adds to the textual embedding the semantics
from the knowledge graph

5. Kernel Interaction Model

6. Kernel Interaction Model

7. Kernel Interaction Model

8. Kernel Interaction Model

9. Kernel Interaction Model

10. Kernel Interaction Model

11. Experimental Setup
• Two datasets
• New York Times (~ 500k articles with summaries)
• e in article is salient if it's also in summary
• Semantic Scholar (~ 1m abstracts)
• e in abstract is salient if it's also in title
• Ranking-focused metrics: P@1, P@5, R@1, R@5
• Well documented parameter settings

12. Results: Entity Salience

13. Analysis VS. Frequency
• KESM is able
to model
salience of
tail entities
• KESM is
more reliable
on short
documents

14. Salience for Ad hoc Search
• Entity salience should model a better text
understanding
• So it should hep for document search
• Ranking uses the salience of the query entities in a
candidate document
• End-to-end training with sufficient relevance labels,
or using model pre-trained on salience

15. Salience-based Ranking

16. Results: Ad hoc Search

20. • Figures taken from these slides:
http://www.cs.cmu.edu/~cx/slides/KESM-slides.pdf