CONFIDENCE MEASURE ESTIMATION FOR OPEN INFORMATION EXTRACTION

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

RESHADAT VAHIDEH; HOURALI MARYAM; FAILI HESHAAM

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: Journal of Information Systems and Telecommunication Year:2018 | Volume:6 | Issue:1 Page(s): 1-8

Download Full-Text

View:

335

Download:

196

Cites:

Information Journal Paper

Title

CONFIDENCE MEASURE ESTIMATION FOR OPEN INFORMATION EXTRACTION

Author(s)

RESHADAT VAHIDEH | HOURALI MARYAM | FAILI HESHAAM | Issue Writer Certificate

Keywords

INFORMATION EXTRACTION

OPEN INFORMATION EXTRACTION

RELATION EXTRACTION

KNOWLEDGE DISCOVERY

FACT EXTRACTION

Abstract

The prior RELATION EXTRACTION approaches were relation-specific and supervised, yielding new instances of relations known a priori. While effective, this model is not applicable in case when the number of relations is high or where the relations are not known a priori. Open INFORMATION EXTRACTION (OIE) is a relation-independent extraction paradigm designed to extract relations directly from massive and heterogeneous corpora such as Web. One of the main challenges for an Open IE system is estimating the probability that its extracted relation is correct. A confidence measure shows that how an extracted relation is a correct instance of a relation among entities. This paper proposes a new method of confidence estimation for OIE called Relation Confidence Estimator for Open INFORMATION EXTRACTION (RCE-OIE). It investigates the incorporation of some proposed features in assigning confidence metric using logistic regression. These features consider diverse lexical, syntactic and semantic knowledge and also some extraction properties such as number of distinct documents from which extractions are drawn, number of relation arguments and their types. We implemented proposed confidence measure on the Open IE systems’ extractions and examined how it affects the performance of results. Evaluations show that incorporation of designed features is promising and the accuracy of our method is higher than the base methods while keeping almost the same performance as them. We also demonstrate how semantic information such as coherence measures can be used in feature-based confidence estimation of Open RELATION EXTRACTION (ORE) to further improve the performance.

Multimedia

No record.

Cites

No record.

References

No record.

Cite

APA: Copy

RESHADAT, VAHIDEH, HOURALI, MARYAM, & FAILI, HESHAAM. (2018). CONFIDENCE MEASURE ESTIMATION FOR OPEN INFORMATION EXTRACTION. JOURNAL OF INFORMATION SYSTEMS AND TELECOMMUNICATION (JIST), 6(1), 1-8. SID. https://sid.ir/paper/332720/en

Vancouver: Copy

RESHADAT VAHIDEH, HOURALI MARYAM, FAILI HESHAAM. CONFIDENCE MEASURE ESTIMATION FOR OPEN INFORMATION EXTRACTION. JOURNAL OF INFORMATION SYSTEMS AND TELECOMMUNICATION (JIST)[Internet]. 2018;6(1):1-8. Available from: https://sid.ir/paper/332720/en

IEEE: Copy

VAHIDEH RESHADAT, MARYAM HOURALI, and HESHAAM FAILI, “CONFIDENCE MEASURE ESTIMATION FOR OPEN INFORMATION EXTRACTION,” JOURNAL OF INFORMATION SYSTEMS AND TELECOMMUNICATION (JIST), vol. 6, no. 1, pp. 1–8, 2018, [Online]. Available: https://sid.ir/paper/332720/en

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops