• Corpus ID: 74065

Open Language Learning for Information Extraction

@inproceedings{Mausam2012OpenLL,
  title={Open Language Learning for Information Extraction},
  author={Mausam and Michael Schmitz and Stephen Soderland and Robert Bart and Oren Etzioni},
  booktitle={Conference on Empirical Methods in Natural Language Processing},
  year={2012}
}
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 sentences. However, state-of-the-art Open IE systems such as ReVerb and woe share two important weaknesses -- (1) they extract only relations that are mediated by verbs, and (2) they ignore context, thus extracting tuples that are not asserted as factual. This paper presents ollie, a substantially… 
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762 Citations
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762 Citations

Open Information Extraction Systems and Downstream Applications

  • Mausam
  • Computer Science
    IJCAI
  • 2016
A decade of progress on building Open IE extractors is described, which results in the latest extractor, OPENIE4, which is computationally efficient, outputs n-ary and nested relations, and also outputs relations mediated by nouns in addition to verbs.

Extraction Systems and Downstream Applications

A decade of progress on building Open IE extractors is described, which results in the latest extractor, OPENIE4, which is computationally efficient, outputs n-ary and nested relations, and also outputs relations mediated by nouns in addition to verbs.

Open Information Extraction

This paper describes an overview of two Open IE generations including strengths, weaknesses and application areas and exposes simple yet principled ways in which verbs express relationships in linguistics such as verb phrase- based extraction or clause-based extraction.

Nested Propositions in Open Information Extraction

NESTIE is proposed, which uses a nested representation to extract higher-order relations, and complex, interdependent assertions, and Nesting the extracted propositions allows NESTIE to more accurately reflect the meaning of the original sentence.

Open Information Extraction with Global Structure Constraints

A novel open IE system, called ReMine, is proposed, which integrates local context signal and global structural signal in a unified framework with distant supervision and can effectively score sentence-level tuple extractions based on corpus-level statistics.

Pattern Learning for Chinese Open Information Extraction

PLCOIE can extract binary relation triples as well as N-ary relation tuples, and experiments show that the results are more precise than state-of-the-art Chinese OIE systems, which indicate that P LCOIE is feasible and effective.

Integrating Local Context and Global Cohesiveness for Open Information Extraction

This paper proposes a novel Open IE system, called ReMine, which integrates local context signals and global structural signals in a unified, distant-supervision framework that can be applied to many different domains to facilitate sentence-level tuple extractions using corpus-level statistics.

A Language Model for Extracting Implicit Relations

IMPLIE (Implicit relation Information Extraction) is presented, that uses an open-domain syntactic language model and user-supplied semantic taggers to overcome this limitation of implicit relations.

Leveraging Linguistic Structure For Open Domain Information Extraction

This work replaces this large pattern set with a few patterns for canonically structured sentences, and shifts the focus to a classifier which learns to extract self-contained clauses from longer sentences to determine the maximally specific arguments for each candidate triple.

Boosting Open Information Extraction with Noun-Based Relations

This work presents a novel Open IE approach that extracts relations expressed in noun compounds, such as (oil, extracted from, olive) from “olive oil”, or in adjective-noun pairs (ANs),such as (moon, that is, gorgeous) from“gorgeous moon”.
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References

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Identifying Relations for Open Information Extraction

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 Information Extraction Using Wikipedia

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 from the Web

Open IE (OIE), a new extraction paradigm where the system makes a single data-driven pass over its corpus and extracts a large set of relational tuples without requiring any human input, is introduced.

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The second generation of Open IE systems are described, which rely on a novel model of how relations and their arguments are expressed in English sentences to double precision/recall compared with previous systems such as TEXTRUNNER and WOE.

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