2 code implementations • 6 Apr 2017 • Ari Kobren, Nicholas Monath, Akshay Krishnamurthy, Andrew McCallum
Many modern clustering methods scale well to a large number of data items, N, but not to a large number of clusters, K. This paper introduces PERCH, a new non-greedy algorithm for online hierarchical clustering that scales to both massive N and K--a problem setting we term extreme clustering.
no code implementations • 29 Jun 2018 • Bo Xiao, Nicholas Monath, Shankar Ananthakrishnan, Abishek Ravi
We first binarize play durations to obtain implicit positive and negative affinity labels.
no code implementations • AKBC 2019 • Ari Kobren, Nicholas Monath, Andrew McCallum
Users have tremendous potential to aid in the construction and maintenance of knowledges bases (KBs) through the contribution of feedback that identifies incorrect and missing entity attributes and relations.
no code implementations • NeurIPS 2018 • Craig Greenberg, Nicholas Monath, Ari Kobren, Patrick Flaherty, Andrew Mcgregor, Andrew McCallum
For many classic structured prediction problems, probability distributions over the dependent variables can be efficiently computed using widely-known algorithms and data structures (such as forward-backward, and its corresponding trellis for exact probability distributions in Markov models).
1 code implementation • 19 Jun 2019 • Nishant Yadav, Ari Kobren, Nicholas Monath, Andrew McCallum
Thus we introduce an approach to supervised hierarchical clustering that smoothly interpolates between single, average, and complete linkage, and we give a training procedure that simultaneously learns a linkage function and a dissimilarity function.
1 code implementation • ACL 2019 • Derek Tam, Nicholas Monath, Ari Kobren, Aaron Traylor, Rajarshi Das, Andrew McCallum
We evaluate STANCE's ability to detect whether two strings can refer to the same entity--a task we term alias detection.
1 code implementation • 31 Dec 2019 • Nicholas Monath, Ari Kobren, Akshay Krishnamurthy, Michael Glass, Andrew McCallum
We introduce Grinch, a new algorithm for large-scale, non-greedy hierarchical clustering with general linkage functions that compute arbitrary similarity between two point sets.
no code implementations • AKBC 2020 • Derek Tam, Nicholas Monath, Ari Kobren, Andrew McCallum
The hierarchical structure of research organizations plays a pivotal role in science of science research as well as in tools that track the research achievements and output.
1 code implementation • 26 Feb 2020 • Craig S. Greenberg, Sebastian Macaluso, Nicholas Monath, Ji-Ah Lee, Patrick Flaherty, Kyle Cranmer, Andrew Mcgregor, Andrew McCallum
In contrast to existing methods, we present novel dynamic-programming algorithms for \emph{exact} inference in hierarchical clustering based on a novel trellis data structure, and we prove that we can exactly compute the partition function, maximum likelihood hierarchy, and marginal probabilities of sub-hierarchies and clusters.
1 code implementation • AKBC 2020 • Dung Thai, Zhiyang Xu, Nicholas Monath, Boris Veytsman, Andrew McCallum
In this paper, we describe a technique for using BibTeX to generate, automatically, a large-scale 41M labeled strings), labeled dataset, that is four orders of magnitude larger than the current largest CFE dataset, namely the UMass Citation Field Extraction dataset [Anzaroot and McCallum, 2013].
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Rajarshi Das, Ameya Godbole, Nicholas Monath, Manzil Zaheer, Andrew McCallum
A case-based reasoning (CBR) system solves a new problem by retrieving `cases' that are similar to the given problem.
Ranked #1 on Link Prediction on NELL-995
no code implementations • NAACL 2021 • Rico Angell, Nicholas Monath, Sunil Mohan, Nishant Yadav, Andrew McCallum
In this paper, we introduce a model in which linking decisions can be made not merely by linking to a knowledge base entity but also by grouping multiple mentions together via clustering and jointly making linking predictions.
2 code implementations • 22 Oct 2020 • Nicholas Monath, Avinava Dubey, Guru Guruganesh, Manzil Zaheer, Amr Ahmed, Andrew McCallum, Gokhan Mergen, Marc Najork, Mert Terzihan, Bryon Tjanaka, YuAn Wang, Yuchen Wu
The applicability of agglomerative clustering, for inferring both hierarchical and flat clustering, is limited by its scalability.
no code implementations • 14 Feb 2021 • Ethan Shen, Maria Brbic, Nicholas Monath, Jiaqi Zhai, Manzil Zaheer, Jure Leskovec
In this paper, we present a comprehensive empirical study on graph embedded few-shot learning.
no code implementations • 14 Apr 2021 • Craig S. Greenberg, Sebastian Macaluso, Nicholas Monath, Avinava Dubey, Patrick Flaherty, Manzil Zaheer, Amr Ahmed, Kyle Cranmer, Andrew McCallum
In those cases, hierarchical clustering can be seen as a combinatorial optimization problem.
1 code implementation • 2 Sep 2021 • Dhruv Agarwal, Rico Angell, Nicholas Monath, Andrew McCallum
Previous work has shown promising results in performing entity linking by measuring not only the affinities between mentions and entities but also those amongst mentions.
1 code implementation • NeurIPS 2021 • Michael Boratko, Dongxu Zhang, Nicholas Monath, Luke Vilnis, Kenneth Clarkson, Andrew McCallum
While vectors in Euclidean space can theoretically represent any graph, much recent work shows that alternatives such as complex, hyperbolic, order, or box embeddings have geometric properties better suited to modeling real-world graphs.
1 code implementation • 17 Dec 2021 • Archan Ray, Nicholas Monath, Andrew McCallum, Cameron Musco
Approximation methods reduce this quadratic complexity, often by using a small subset of exactly computed similarities to approximate the remainder of the complete pairwise similarity matrix.
no code implementations • 15 Sep 2022 • Somnath Basu Roy Chowdhury, Nicholas Monath, Avinava Dubey, Amr Ahmed, Snigdha Chaturvedi
We then use these representations to quantify the relevance of review sentences using a novel approximate geodesic distance based scoring mechanism.
1 code implementation • 7 Oct 2022 • Kumar Shridhar, Nicholas Monath, Raghuveer Thirukovalluru, Alessandro Stolfo, Manzil Zaheer, Andrew McCallum, Mrinmaya Sachan
Ontonotes has served as the most important benchmark for coreference resolution.
1 code implementation • 23 Oct 2022 • Nishant Yadav, Nicholas Monath, Rico Angell, Manzil Zaheer, Andrew McCallum
When the similarity is measured by dot-product between dual-encoder vectors or $\ell_2$-distance, there already exist many scalable and efficient search methods.
1 code implementation • 26 Oct 2022 • Tianyu Liu, Yuchen Jiang, Nicholas Monath, Ryan Cotterell, Mrinmaya Sachan
Recent years have seen a paradigm shift in NLP towards using pretrained language models ({PLM}) for a wide range of tasks.
Ranked #1 on Relation Extraction on CoNLL04 (RE+ Micro F1 metric)
no code implementations • 27 Mar 2023 • Nicholas Monath, Manzil Zaheer, Kelsey Allen, Andrew McCallum
First, we introduce an algorithm that uses a tree structure to approximate the softmax with provable bounds and that dynamically maintains the tree.
1 code implementation • 4 May 2023 • Nishant Yadav, Nicholas Monath, Manzil Zaheer, Andrew McCallum
While ANNCUR's one-time selection of anchors tends to approximate the cross-encoder distances on average, doing so forfeits the capacity to accurately estimate distances to items near the query, leading to regret in the crucial end-task: recall of top-k items.
1 code implementation • 17 Oct 2023 • Somnath Basu Roy Chowdhury, Nicholas Monath, Ahmad Beirami, Rahul Kidambi, Avinava Dubey, Amr Ahmed, Snigdha Chaturvedi
In the online setting, where the algorithm has access to a single instance at a time, estimating the group fairness objective requires additional storage and significantly more computation (e. g., forward/backward passes) than the task-specific objective at every time step.
1 code implementation • NeurIPS 2023 • Somnath Basu Roy Chowdhury, Nicholas Monath, Avinava Dubey, Amr Ahmed, Snigdha Chaturvedi
Distributed representations provide a vector space that captures meaningful relationships between data instances.
no code implementations • 16 Jan 2024 • Somnath Basu Roy Chowdhury, Nicholas Monath, Avinava Dubey, Manzil Zaheer, Andrew McCallum, Amr Ahmed, Snigdha Chaturvedi
In this work, we study the task of extractive opinion summarization in an incremental setting, where the underlying review set evolves over time.
no code implementations • CRAC (ACL) 2021 • Nishant Yadav, Nicholas Monath, Rico Angell, Andrew McCallum
Coreference decisions among event mentions and among co-occurring entity mentions are highly interdependent, thus motivating joint inference.
no code implementations • ACL (RepL4NLP) 2021 • Raghuveer Thirukovalluru, Mukund Sridhar, Dung Thai, Shruti Chanumolu, Nicholas Monath, Sankaranarayanan Ananthakrishnan, Andrew McCallum
Specially, neural semantic parsers (NSPs) effectively translate natural questions to logical forms, which execute on KB and give desirable answers.
1 code implementation • NAACL 2022 • Dhruv Agarwal, Rico Angell, Nicholas Monath, Andrew McCallum
Learning representations of entity mentions is a core component of modern entity linking systems for both candidate generation and making linking predictions.
Ranked #1 on Entity Linking on ZESHEL