Leveraging Linguistic Structure For Open Domain Information Extraction

@inproceedings{Angeli2015LeveragingLS,
  title={Leveraging Linguistic Structure For Open Domain Information Extraction},
  author={Gabor Angeli and Melvin Johnson and Christopher D. Manning},
  booktitle={ACL},
  year={2015}
}
Relation triples produced by open domain information extraction (open IE) systems are useful for question answering, inference, and other IE tasks. Traditionally these are extracted using a large set of patterns; however, this approach is brittle on out-of-domain text and long-range dependencies, and gives no insight into the substructure of the arguments. We replace this large pattern set with a few patterns for canonically structured sentences, and shift the focus to a classifier which learns… 

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References

SHOWING 1-10 OF 54 REFERENCES
Adapting Open Information Extraction to Domain-Specific Relations
TLDR
The steps needed to adapt Open IE to a domain-specific ontology are explored and the approach of mapping domain-independent tuples to an ontology using domains from DARPA’s Machine Reading Project is demonstrated.
Open Language Learning for Information Extraction
Open Information Extraction (IE) systems extract relational tuples from text, without requiring a pre-specified vocabulary, by identifying relation phrases and associated arguments in arbitrary
Identifying Relations for Open Information Extraction
TLDR
Two simple syntactic and lexical constraints on binary relations expressed by verbs are introduced in the ReVerb Open IE system, which more than doubles the area under the precision-recall curve relative to previous extractors such as TextRunner and woepos.
Open question answering over curated and extracted knowledge bases
TLDR
This paper presents OQA, the first approach to leverage both curated and extracted KBs, and demonstrates that it achieves up to twice the precision and recall of a state-of-the-art Open QA system.
Open Information Extraction Using Wikipedia
TLDR
WOE is presented, an open IE system which improves dramatically on TextRunner's precision and recall and is a novel form of self-supervised learning for open extractors -- using heuristic matches between Wikipedia infobox attribute values and corresponding sentences to construct training data.
Open Information Extraction to KBP Relations in 3 Hours
We participated in both the English Slot Filling and Entity Linking in the 2013 TAC-KBP evaluation. Our Slot Filling system provides an answer to the following conjectures: Can Open Information
Effectiveness and Efficiency of Open Relation Extraction
TLDR
A fair and objective experimental comparison of 8 state-of-the-art approaches over 5 different datasets is presented, and sheds some light on the tradeoff between NLP depth and effectiveness.
Distant supervision for relation extraction without labeled data
TLDR
This work investigates an alternative paradigm that does not require labeled corpora, avoiding the domain dependence of ACE-style algorithms, and allowing the use of corpora of any size.
Combining Distant and Partial Supervision for Relation Extraction
TLDR
This work presents an approach for providing partial supervision to a distantly supervised relation extractor using a small number of carefully selected examples, and proposes a novel criterion to sample examples which are both uncertain and representative.
Learning text analysis rules for domain-specific natural language processing
TLDR
This thesis presents CRYSTAL, an implemented system that automatically induces domain-specific text analysis rules from training examples that approach the performance of hand-coded rules, are robust in the face of noise and inadequate features, and require only a modest amount of training data.
...
...