paint-brush
Examples of Recovering from Entity Linking Errorsby@fewshot
109 reads

Examples of Recovering from Entity Linking Errors

by The FewShot Prompting Publication
The FewShot Prompting Publication  HackerNoon profile picture

The FewShot Prompting Publication

@fewshot

Spearheading research, publications, and advancements in few-shot learning, and redefining...

June 23rd, 2024
Read on Terminal Reader
Read this story in a terminal
Print this story
Read this story w/o Javascript
Read this story w/o Javascript
tldt arrow

Too Long; Didn't Read

Here, we illustrate our proposal of using entity mentions to recover from entity linking errors. In the training set, we have the following example:
featured image - Examples of Recovering from Entity
Linking Errors
1x
Read by Dr. One voice-avatar

Listen to this story

The FewShot Prompting Publication  HackerNoon profile picture
The FewShot Prompting Publication

The FewShot Prompting Publication

@fewshot

Spearheading research, publications, and advancements in few-shot learning, and redefining artificial intelligence.

About @fewshot
LEARN MORE ABOUT @FEWSHOT'S
EXPERTISE AND PLACE ON THE INTERNET.
0-item

STORY’S CREDIBILITY

Academic Research Paper

Academic Research Paper

Part of HackerNoon's growing list of open-source research papers, promoting free access to academic material.

Authors:

(1) Silei Xu, Computer Science Department, Stanford University Stanford, CA with equal contribution {silei@cs.stanford.edu};

(2) Shicheng Liu, Computer Science Department, Stanford University Stanford, CA with equal contribution {shicheng@cs.stanford.edu};

(3) Theo Culhane, Computer Science Department, Stanford University Stanford, CA {tculhane@cs.stanford.edu};

(4) Elizaveta Pertseva, Computer Science Department, Stanford University Stanford, CA, {pertseva@cs.stanford.edu};

(5) Meng-Hsi Wu, Computer Science Department, Stanford University Stanford, CA, Ailly.ai {jwu@ailly.ai};

(6) Sina J. Semnani, Computer Science Department, Stanford University Stanford, CA, {sinaj@cs.stanford.edu};

(7) Monica S. Lam, Computer Science Department, Stanford University Stanford, CA, {lam@cs.stanford.edu}.

Abstract and Introduction

Related Work

Semantic Parsing for Wikidata

WikiWebQuestions (WWQ) Dataset

Implementation

Experiments

Experiment with QALD-7

Conclusions, Limitations, Ethical Considerations, Acknowledgements, and References

A. Examples of Recovering from Entity Linking Errors

A. Examples of Recovering from Entity Linking Errors

Here, we illustrate our proposal of using entity mentions to recover from entity linking errors. In the training set, we have the following example:


• Query: What year did giants win the world series?


• Original Gold SPARQL:


SELECT DISTINCT ?x WHERE { ?y wdt:sports_season_of_league_or_competition wd:Q265538; wdt:winner wd:Q308966; wdt:point_in_time ?x. }


• Gold Entity linker result:


World Series (QID Q265538),

San Francisco Giants (QID Q308966);


• ReFinED result:


San Francisco Giants (QID Q308966);


Here, the ReFinED entity linker model fails to identify the “World Series” entity. Our proposal of mentions gives the semantic parser a chance to recover from entity linker failures. To train the parser to generate mentions, our training includes samples like this:


• Query: what year did giants win the world series?


• ReFinED result:


San Francisco Giants (QID Q308966);


• Gold target:


SELECT DISTINCT ?x WHERE { ?y wdt:sports_season_of_league_or_competition; wd:world_series; wdt:winner wd:Q308966; wdt:point_in_time ?x. }


The gold query mentions “world_series”. At inference time, our heuristics use the predicted mention to look up the actual Wikidata entity. For example, if wd:world_series is predicted at inference time, our heuristics maps it back to wd:Q265538.


This paper is available on arxiv under CC 4.0 license.


L O A D I N G
. . . comments & more!

About Author

The FewShot Prompting Publication  HackerNoon profile picture
The FewShot Prompting Publication @fewshot
Spearheading research, publications, and advancements in few-shot learning, and redefining artificial intelligence.

TOPICS

THIS ARTICLE WAS FEATURED IN...

Permanent on Arweave
Read on Terminal Reader
Read this story in a terminal
 Terminal
Read this story w/o Javascript
Read this story w/o Javascript
 Lite
X REMOVE AD