no code implementations • 14 Dec 2016 • Yike Liu, Tara Safavi, Abhilash Dighe, Danai Koutra
While advances in computing resources have made processing enormous amounts of data possible, human ability to identify patterns in such data has not scaled accordingly.
1 code implementation • 17 Feb 2018 • Mark Heimann, Haoming Shen, Tara Safavi, Danai Koutra
Problems involving multiple networks are prevalent in many scientific and other domains.
Social and Information Networks
no code implementations • EMNLP 2020 • Tara Safavi, Danai Koutra, Edgar Meij
We first conduct an evaluation under the standard closed-world assumption (CWA), in which predicted triples not already in the knowledge graph are considered false, and show that existing calibration techniques are effective for KGE under this common but narrow assumption.
no code implementations • 18 Jun 2020 • Edgar Meij, Tara Safavi, Chenyan Xiong, Gianluca Demartini, Miriam Redi, Fatma Özcan
The KG-BIAS 2020 workshop touches on biases and how they surface in knowledge graphs (KGs), biases in the source data that is used to create KGs, methods for measuring or remediating bias in KGs, but also identifying other biases such as how and which languages are represented in automatically constructed KGs or how personal KGs might incur inherent biases.
2 code implementations • EMNLP 2020 • Tara Safavi, Danai Koutra
We present CoDEx, a set of knowledge graph completion datasets extracted from Wikidata and Wikipedia that improve upon existing knowledge graph completion benchmarks in scope and level of difficulty.
Ranked #2 on Link Prediction on CoDEx Large
1 code implementation • EMNLP 2021 • Tara Safavi, Jing Zhu, Danai Koutra
Codifying commonsense knowledge in machines is a longstanding goal of artificial intelligence.
no code implementations • EMNLP 2021 • Tara Safavi, Danai Koutra
Relational knowledge bases (KBs) are commonly used to represent world knowledge in machines.
1 code implementation • 16 May 2022 • Tara Safavi, Doug Downey, Tom Hope
Knowledge graph (KG) link prediction is a fundamental task in artificial intelligence, with applications in natural language processing, information retrieval, and biomedicine.
no code implementations • 14 Sep 2023 • Chirag Shah, Ryen W. White, Reid Andersen, Georg Buscher, Scott Counts, Sarkar Snigdha Sarathi Das, Ali Montazer, Sathish Manivannan, Jennifer Neville, Xiaochuan Ni, Nagu Rangan, Tara Safavi, Siddharth Suri, Mengting Wan, Leijie Wang, Longqi Yang
However, using LLMs to generate a user intent taxonomy and apply it for log analysis can be problematic for two main reasons: (1) such a taxonomy is not externally validated; and (2) there may be an undesirable feedback loop.
no code implementations • 16 Sep 2023 • Sarkar Snigdha Sarathi Das, Chirag Shah, Mengting Wan, Jennifer Neville, Longqi Yang, Reid Andersen, Georg Buscher, Tara Safavi
The traditional Dialogue State Tracking (DST) problem aims to track user preferences and intents in user-agent conversations.
no code implementations • 15 Nov 2023 • Sheshera Mysore, Zhuoran Lu, Mengting Wan, Longqi Yang, Steve Menezes, Tina Baghaee, Emmanuel Barajas Gonzalez, Jennifer Neville, Tara Safavi
Powerful large language models have facilitated the development of writing assistants that promise to significantly improve the quality and efficiency of composition and communication.
no code implementations • 18 Mar 2024 • Mengting Wan, Tara Safavi, Sujay Kumar Jauhar, Yujin Kim, Scott Counts, Jennifer Neville, Siddharth Suri, Chirag Shah, Ryen W White, Longqi Yang, Reid Andersen, Georg Buscher, Dhruv Joshi, Nagu Rangan
Transforming unstructured text into structured and meaningful forms, organized by useful category labels, is a fundamental step in text mining for downstream analysis and application.
no code implementations • 19 Mar 2024 • Ying-Chun Lin, Jennifer Neville, Jack W. Stokes, Longqi Yang, Tara Safavi, Mengting Wan, Scott Counts, Siddharth Suri, Reid Andersen, Xiaofeng Xu, Deepak Gupta, Sujay Kumar Jauhar, Xia Song, Georg Buscher, Saurabh Tiwary, Brent Hecht, Jaime Teevan
Accurate and interpretable user satisfaction estimation (USE) is critical for understanding, evaluating, and continuously improving conversational systems.
no code implementations • 19 Mar 2024 • Siddharth Suri, Scott Counts, Leijie Wang, Chacha Chen, Mengting Wan, Tara Safavi, Jennifer Neville, Chirag Shah, Ryen W. White, Reid Andersen, Georg Buscher, Sathish Manivannan, Nagu Rangan, Longqi Yang
Until recently, search engines were the predominant method for people to access online information.