Poster for our EMNLP paper on extracting non-standard relations from the Web with distant supervision and imitation learning. Read the full paper here: https://aclweb.org/anthology/D/D15/D15-1086.pdf

1. Isabelle Augenstein, Andreas
Vlachos, Diana Maynard
Extracting Relations between Non-Standard Entities using
Distant Supervision and Imitation Learning
Approach Overview
Musical Artist Album
Michael Jackson Music & Me
The Beatles ?
Stealers Wheel ?
Sentences
Preview songs from Forever, Michael by Michael Jackson on the iTunes Store.
The only Beatles album to occasion negative, even hostile reviews, there are
few other rock records as controversial as Let It Be.
The Beatles recorded ten songs during a single studio session for their debut
LP, Please Please Me.
Stealers Wheel are a Scottish folk rock band formed in Paisley, Renfrewshire.
Web Search
Sentences
Music & Me is the third studio album by
American singer Michael Jackson.
Stealers Wheel are a Scottish folk rock
band formed in Paisley, Renfrewshire.
NER
Training Instances
Label Subject Object
true Michael Jackson Forever, Michael
Sentence
Preview songs from
false Michael Jackson iTunes Store Preview songs from
Testing Instances
true The Beatles Let It Be The only Beatles album to
false The Beatles LP The Beatles recorded ten
true The Beatles Please Please Me The Beatles recorded ten
false Stealers Wheel Scottish Stealers Wheel are a
false Stealers Wheel Paisley, Renfrewshire Stealers Wheel are a
Distant
Supervision
NE Classifier
Relation Extractor
Train
Predict
Output
Subject Object
The Beatles Let It Be
The Beatles Please Please Me
Stealers Wheel
Distant Supervision
Train relation extractors without manually labeled
data, using a knowledge base
and unlabeled text
How can we recognise arguments of
relations?
Named Entity Recognition (NER) and Named Entity Classification
(NEC) are typically used as part of preprocessing using tools such
as Stanford NER or FIGER
Is off-the-shelf NER good
enough?
• Experiments with 16 relations (e.g. album,
character, record label, author, origin)
Recall of Stanford NER compared to simple
POS-based heuristics
Improving NEC for RE: Imitation Learning
• Simple solution (OS): adding NEC features to RE
• Problem: NEC features (e.g. mention, mention context)
overpower RE features (e.g. path between s and o)
Ø OS would incorrectly predict Steven Spielberg,
because context is stronger
• Solution (IL): Decomposing the learning task into series of
actions: NEC, then RE if NEC prediction is positive
• Classifiers are trained iteratively with imitation learning
algorithm DAGGER (Ross et al., 2011)
• NEC stage is fairly permissive and enhances RE
Ø NEC prediction for both candidates is positive
Ø RE correctly predicts Alfred Hitchcock
Improving NEC for RE: Web Features
Results
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P-­‐avg
Rel
only
Stanf
FIGER
OS
IL
One of director Steven Spielberg’s greatest heroes was
Alfred Hitchcock, the mastermind behind Psycho.
Candidates for director relation with subject Psycho:
Steven Spielberg, Alfred Hitchcock
Arctic Monkeys
Arctic Monkeys are a rock band from Sheffield,
famous for albums such as AM.
Albums:
- Whatever People Say I Am, That's What I'm Not
- AM
header
link
bold
list
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0.5
1
PER
LOC
ORG
MISC
Stanford
NER
HeurisHc
Conclusions
• Imitation learning approach outperforms baselines with supervised NEC
(Stanford NER and FIGER) by 10 points in average precision
• Web features such as appearance in lists or links to other Web improve
average precision by 7 points
• Sparse, high-precision features (such as parse) outperform high-recall low-
precision features (such as BOW features)
References
Stéphane Ross, Geoffrey J. Gordon, and Drew Bagnell.
2011. A Reduction of Imitation Learning and Structured
Prediction to No-Regret Online Learning. JMLR.