no code implementations • 11 Sep 2023 • Wei Du, Laksh Advani, Yashmeet Gambhir, Daniel J Perry, Prashant Shiralkar, Zhengzheng Xing, Aaron Colak
For industry applications, it is imperative to assess the performance of the LLM on unlabeled production data from time to time to validate for a real-world setting.
no code implementations • 23 May 2023 • Yinghao Li, Colin Lockard, Prashant Shiralkar, Chao Zhang
To establish such connections, we propose to extract PTs from the Web pages containing hand-crafted PT recommendations for SIs.
no code implementations • 27 Aug 2022 • Ritesh Sarkhel, Binxuan Huang, Colin Lockard, Prashant Shiralkar
Prior works rely on a few human-labeled web pages from each target website or thousands of human-labeled web pages from some seed websites to train a transferable extraction model that generalizes on unseen target websites.
1 code implementation • 25 Jan 2022 • Xiang Deng, Prashant Shiralkar, Colin Lockard, Binxuan Huang, Huan Sun
We argue that the text and HTML structure together convey important semantics of the content and therefore warrant a special treatment for their representation learning.
Ranked #2 on Attribute Extraction on SWDE
1 code implementation • 17 Feb 2021 • Daheng Wang, Prashant Shiralkar, Colin Lockard, Binxuan Huang, Xin Luna Dong, Meng Jiang
Existing work linearize table cells and heavily rely on modifying deep language models such as BERT which only captures related cells information in the same table.
no code implementations • ACL 2020 • Xin Luna Dong, Hannaneh Hajishirzi, Colin Lockard, Prashant Shiralkar
In this tutorial we take a holistic view toward information extraction, exploring the commonalities in the challenges and solutions developed to address these different forms of text.
no code implementations • 14 May 2020 • Colin Lockard, Prashant Shiralkar, Xin Luna Dong, Hannaneh Hajishirzi
In this work, we propose a solution for "zero-shot" open-domain relation extraction from webpages with a previously unseen template, including from websites with little overlap with existing sources of knowledge for distant supervision and websites in entirely new subject verticals.
1 code implementation • ACL 2020 • Bill Yuchen Lin, Dong-Ho Lee, Ming Shen, Ryan Moreno, Xiao Huang, Prashant Shiralkar, Xiang Ren
In this paper, we introduce "entity triggers," an effective proxy of human explanations for facilitating label-efficient learning of NER models.
no code implementations • NAACL 2019 • Colin Lockard, Prashant Shiralkar, Xin Luna Dong
In this paper, we define the problem of OpenIE from semi-structured websites to extract such facts, and present an approach for solving it.
no code implementations • 12 Apr 2018 • Colin Lockard, Xin Luna Dong, Arash Einolghozati, Prashant Shiralkar
In this paper we present a new method for automatic extraction from semi-structured websites based on distant supervision.
1 code implementation • 24 Aug 2017 • Prashant Shiralkar, Alessandro Flammini, Filippo Menczer, Giovanni Luca Ciampaglia
The volume and velocity of information that gets generated online limits current journalistic practices to fact-check claims at the same rate.
no code implementations • 20 Jan 2016 • V. S. Subrahmanian, Amos Azaria, Skylar Durst, Vadim Kagan, Aram Galstyan, Kristina Lerman, Linhong Zhu, Emilio Ferrara, Alessandro Flammini, Filippo Menczer, Andrew Stevens, Alexander Dekhtyar, Shuyang Gao, Tad Hogg, Farshad Kooti, Yan Liu, Onur Varol, Prashant Shiralkar, Vinod Vydiswaran, Qiaozhu Mei, Tim Hwang
A number of organizations ranging from terrorist groups such as ISIS to politicians and nation states reportedly conduct explicit campaigns to influence opinion on social media, posing a risk to democratic processes.