Bootstrapping for Numerical Open IE

Saha, Swarnadeep; Pal, Harshata; Mausam, Mausam

doi:10.18653/v1/p17-2050

Cited by 82 publications

(65 citation statements)

References 20 publications

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“…OPENIE4 was built on SRLIE with a rule-based extraction system RELNOUN (Pal and Mausam, 2016) for extracting noun-mediated relations. Recently, OPENIE5 improved upon extractions from numerical sentences (Saha et al, 2017) and broke conjunctions in arguments to generate multiple extractions. During this period, there were also some other Open IE systems emerged and successfully applied in different scenarios, such as ClausIE (Del Corro and Gemulla, 2013) Stanford OPENIE (Angeli et al, 2015), PropS , and more.…”

Section: Related Workmentioning

confidence: 99%

Neural Open Information Extraction

Cui¹,

Wei²,

Zhou³

2018

Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

101

128

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Conventional Open Information Extraction (Open IE) systems are usually built on hand-crafted patterns from other NLP tools such as syntactic parsing, yet they face problems of error propagation. In this paper, we propose a neural Open IE approach with an encoder-decoder framework. Distinct from existing methods, the neural Open IE approach learns highly confident arguments and relation tuples bootstrapped from a state-of-the-art Open IE system. An empirical study on a large benchmark dataset shows that the neural Open IE system significantly outperforms several baselines, while maintaining comparable computational efficiency.

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Section: Related Workmentioning

confidence: 99%

Neural Open Information Extraction

Cui¹,

Wei²,

Zhou³

2018

Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

101

128

View full text Add to dashboard Cite

show abstract

“…After transforming questions into statements, we employ the Stanford OpenIE tool [14] and OpenIE5.0 ( [25], [26], [20], [6]) to extract triples from assertive sentences. When several triples with the same S and O are extracted from the same sentence, we retain only the one with the longest P phrase.…”

Section: From Questions To Assertionsmentioning

confidence: 99%

Commonsense Properties from Query Logs and Question Answering Forums

Romero

Razniewski

Pal

et al. 2019

Proceedings of the 28th ACM International Conference on Information and Knowledge Management

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Commonsense knowledge about object properties, human behavior and general concepts is crucial for robust AI applications. However, automatic acquisition of this knowledge is challenging because of sparseness and bias in online sources. This paper presents Quasimodo, a methodology and tool suite for distilling commonsense properties from non-standard web sources. We devise novel ways of tapping into search-engine query logs and QA forums, and combining the resulting candidate assertions with statistical cues from encyclopedias, books and image tags in a corroboration step. Unlike prior work on commonsense knowledge bases, Quasimodo focuses on salient properties that are typically associated with certain objects or concepts. Extensive evaluations, including extrinsic use-case studies, show that Quasimodo provides better coverage than state-of-the-art baselines with comparable quality.

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“…Numbers, a major construct for expressing counts, were investigated mostly in the context of temporal information, e.g. to enrich facts with timestamps/durations [16,30], or in the context of quantities and measures like MtEverest, height, 8848mt [17,11,24,28]. In contrast, terms that express counting quantifiers are either extracted incorrectly by state-of-the-art Open-IE systems, or not at all.…”

Section: Related Workmentioning

confidence: 99%

Enriching Knowledge Bases with Counting Quantifiers

Mirza

Razniewski

Darari

et al. 2018

Lecture Notes in Computer Science

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Information extraction traditionally focuses on extracting relations between identifiable entities, such as Monterey, locatedIn, California . Yet, texts often also contain Counting information, stating that a subject is in a specific relation with a number of objects, without mentioning the objects themselves, for example, "California is divided into 58 counties". Such counting quantifiers can help in a variety of tasks such as query answering or knowledge base curation, but are neglected by prior work. This paper develops the first full-fledged system for extracting counting information from text, called CINEX. We employ distant supervision using fact counts from a knowledge base as training seeds, and develop novel techniques for dealing with several challenges: (i) non-maximal training seeds due to the incompleteness of knowledge bases, (ii) sparse and skewed observations in text sources, and (iii) high diversity of linguistic patterns. Experiments with five human-evaluated relations show that CINEX can achieve 60% average precision for extracting counting information. In a large-scale experiment, we demonstrate the potential for knowledge base enrichment by applying CINEX to 2,474 frequent relations in Wikidata. CINEX can assert the existence of 2.5M facts for 110 distinct relations, which is 28% more than the existing Wikidata facts for these relations. arXiv:1807.03656v1 [cs.CL] 10 Jul 2018Second, an important use case is KB curation [8,34]. KBs are notoriously incomplete, contain erroneous triples, and are limited in keeping up with the pace of real-world changes. Counting information helps to identify gaps and inaccuracies. For example, knowing the exact number of counties in California or a lower bound for the number of films directed by Eastwood are important cues to complete and enrich a KB.State-of-the-Art and Challenges. The predominant approach to extracting facts for KB population is distant supervision, using seeds for the SPO triples of interest (e.g., [21,32]). The seeds are usually taken from an initial KB or are manually compiled. Spotting the seeds in a text corpus (e.g., Clint Eastwood, directed and Gran Torino) then allows learning patterns for relations (e.g., "director of" or " someone 's masterpiece"), which in turn lead to observing new fact candidates. This methodology is known as the pattern-relation duality principle [2].Distant supervision is a natural approach for extracting counting information as well: the cardinality of distinct O arguments for a given SP pair, n := |{O | SP O ∈ KB }|, serves as a seed for the counting assertion, S, P, ∃n . However, it is more challenging than traditional SPO-fact extraction and needs to cope with several issues: 1) Non-maximal seeds: Unlike for SPO-fact extraction, the incompleteness of KBs not only leads to a reduction in the number of seeds, but to seeds that systematically underestimate the count of facts that are valid in reality. For example, a KB that knows only a subset of Trump's children, say three out of five, leads to a non-maximal s...

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Bootstrapping for Numerical Open IE

Cited by 82 publications

References 20 publications

Neural Open Information Extraction

Neural Open Information Extraction

Commonsense Properties from Query Logs and Question Answering Forums

Enriching Knowledge Bases with Counting Quantifiers

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