College of Information and Computer Science
University of Massachusetts Amherst
140 Governors Drive
University of Massachusetts
Amherst, MA 01003
nmonath [at] cs [dot] umass [dot] edu
I am a MS/PhD student in computer science at the University of Massachusetts Amherst advised by Professor Andrew McCallum. I am a member of the Information Extraction and Synthesis Laboratory. My research focuses on machine learning and natural language processing. I received my B.S. in computer science and mathematics from Brandeis University in 2013 where I worked with Professors James Storer and Antonella Di Lillo.
The focus of my research is scalable clustering methods with an application focus on entity resolution. In particular, developing clustering methods that scale to massive numbers of clusters (entities) and points (entity mentions). I am also interested in extending these methods to model dependencies between the cluster (entity) assignments of points (mentions). My research includes models to learn robust entity representations, in particular modeling mention spellings. I am interested in the combination of entity linking and resolution, a problem setting where we are given an initial knowledge base and discover new entities as more data arrives.
University of Massachusetts Amherst.
Started MS (only) Fall 2013
Entered MS/PhD Fall 2015
Brandeis University. BS 2013. Computer Science and Mathematics.
Derek Tam, Nicholas Monath, Ari Kobren, Aaron Traylor, Rajarshi Das, Andrew McCallum. Optimal Transport-based Alignment of Learned Character Representations for String Similarity. ACL. 2019. [arxiv] [pdf] [code + data]
Nicholas Monath*, Ari Kobren*, Akshay Krishnamurthy, Michael Glass, Andrew McCallum. Scalable Hierarchical Clustering via Tree Grafting. KDD. 2019 (Oral presentation) (* Equal Contribution). [pdf] [Code Coming Soon]
Nicholas Monath, Manzil Zaheer, Daniel Silva, Andrew McCallum, Amr Ahmed. Gradient-based Hierarchical Clustering using Continuous Representations of Trees in Hyperbolic Space. KDD. 2019. [pdf] [Code Coming Soon]
Nishant Yadav, Ari Kobren, Nicholas Monath, Andrew McCallum. Supervised Hierarchical Clustering with Exponential Linkage. ICML. 2019. [arxiv] [pdf] [code]
Ari Kobren, Nicholas Monath, Andrew McCallum. Integrating User Feedback under Identity Uncertainty in Knowledge Base Construction. AKBC, 2019. [pdf]
Craig Greenberg, Nicholas Monath, Ari Kobren, Patrick Flaherty, Andrew McGregor, Andrew McCallum. Compact Representation of Uncertainty In Clustering. NeurIPS 2018. [pdf]
Bo Xiao, Nicholas Monath, Shankar Ananthakrishnan, Abishek Ravi. Play Duration based User-Entity Affinity Modeling in Spoken Dialog System Interspeech 2018. [pdf]
Nicholas Monath (*), Ari Kobren (*), Akshay Krishnamurthy, Andrew McCallum. Gradient-based Hierarchical Clustering. NIPS Workshop on Discrete Structures in Machine Learning. 2017. (Oral Presentation). [pdf]
Ari Kobren, Nicholas Monath, Andrew McCallum. Entity-centric Attribute Feedback for Interactive Knowledge Bases. NIPS Workshop on Automated Knowledge Base Construction. 2017. [pdf]
Aaron Traylor (*), Nicholas Monath (*), Rajarshi Das, Andrew McCallum. Learning String Alignments for Entity Aliases. NIPS Workshop on Automated Knowledge Base Construction. 2017. [pdf] [code]
Ari Kobren (*), Nicholas Monath (*), Akshay Krishnamurthy, and Andrew McCallum. A Hierarchical Algorithm for Extreme Clustering . KDD. 2017. (* equal contribution). (Oral Presenation). [pdf] [code] [talk] [promo video]
Haw-Shiuan Chang, Abdurrahman Munir, Ao Liu, Johnny Tian-Zheng Wei, Aaron Traylor, Ajay Nagesh, Nicholas Monath, Patrick Verga, Emma Strubell, and Andrew McCallum. Extracting Multilingual Relations under Limited Resources: TAC 2016 Cold-Start KB construction and Slot-Filling using Compositional Universal Schema. NIST TAC KBP Workshop 2016. Notebook version [pdf]
Benjamin Roth, Nicholas Monath, David Belanger, Emma Strubell, Patrick Verga and Andrew McCallum Building Knowledge Bases with Universal Schema: Cold Start and Slot-Filling Approaches TAC KBP 2015 Workshop [pdf]
Mykel J. Kochenderfer and Nicholas Monath. Data Compression of Optimal Value Functions for Markov Decision Processes Data Compression Conference. Snowbird Utah 2013.
Nicholas Monath and Andrew McCallum. Discriminative Hierarchical Coreference for Inventor Disambiguation. Presentation. PatentsView Inventor Disambiguation Technical Workshop. September 2015 [slides] [code]
1st place. Inventor Disambiguation Challenge. PatentsView Inventor Disambiguation Technical Workshop. September 2015. [link] [slides] [code]. Our inventor name disambiguation system was integrated into USPTO PatentsView website.