Dan Roth

Dan Roth is a person.

• See: IE Algorithm, LBJ Learning System, Wikification.

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References

• Professional Homepage: http://cis.upenn.edu/~danroth/ http://l2r.cs.uiuc.edu/
• DBLP Author Page: http://www.informatik.uni-trier.de/~ley/db/indices/a-tree/r/Roth:Dan.html
• Google Scholar Author Page: http://scholar.google.com/citations?user=WHOHV3AAAAAJ

2023

• (Min et al., 2023) ⇒ Bonan Min, Hayley Ross, Elior Sulem, Amir Pouran Ben Veyseh, Thien Huu Nguyen, Oscar Sainz, Eneko Agirre, Ilana Heintz, and Dan Roth. (2023). “Recent Advances in Natural Language Processing via Large Pre-trained Language Models: A Survey.” In: ACM Computing Surveys, 56(2).

2018

• http://www.cis.upenn.edu/~danroth/
  • QUOTE: My research focuses on the computational foundations of intelligent behavior. We develop theories and systems pertaining to intelligent behavior using a unified methodology -- at the heart of which is the idea that learning has a central role in intelligence.

    My work centers around the study of machine learning and inference methods to facilitate natural language understanding. In doing that I have pursued several interrelated lines of work that span multiple aspects of this problem - from fundamental questions in learning and inference and how they interact, to the study of a range of natural language processing (NLP) problems. Over the last few years the focus of my natural language understanding work has been the development of constrained conditional models -- an integer linear programming formulation for (jointly) learning and supporting global inference. Within this framework we have studied fundamental learning and inference issues -- from learning with indirect supervision to response driven learning to decomposed learning to amortized inference -- and addressed multiple problems in semantics and information extraction. In particular, we developed state of the art solutions and systems for semantic role labeling, co-reference resolutions, and textual entailment as well as named entity recognition, Wikification and other information extraction problems. A lot of my recent work has also emphasized the notion of incidental supervision as a way to get around the inherent difficulty in supervising complex problems. I have also worked on fundamental problems in Natural Language Acquisition, ESL, and Information Trustworthiness. Over the last decade we have also developed a declarative Learning Based Programming language, LBJava, for the rapid development of software systems with learned components; we are currently working on Saul, a next generation Declarative Learning Based Program.

2016

• (Tsai & Roth, 2016) ⇒ Chen-Tse Tsai, and Dan Roth. (2016). “Cross-lingual Wikification Using Multilingual Embeddings.” In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies.

2014

• (Roth et al., 2014) ⇒ Dan Roth, Heng Ji, Ming‐Wei Chang, and Taylor Cassidy. (2014). “Wikification and Beyond: The Challenges of Entity and Concept Grounding." Tutorial at ACL 2014.

2013

• (Dagan et al., 2013) ⇒ Ido Dagan, Dan Roth, Mark Sammons, and Fabio Massimo Zanzotto. (2013). “Recognizing Textual Entailment: Models and Applications." Morgan \& Claypool Publishers. doi:10.2200/S00509ED1V01Y201305HLT023

2011

• (Vydiswaran et al., 2011) ⇒ V.G. Vinod Vydiswaran, ChengXiang Zhai, and Dan Roth. (2011). “Content-driven Trust Propagation Framework.” In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2011). doi:10.1145/2020408.2020567

2010

• (Rizzolo & Roth, 2010) ⇒ Nick Rizzolo, and Dan Roth. (2010). “Learning Based Java for Rapid Development of NLP Systems.” In: Proceedings of the International Conference on Language Resources and Evaluation (LREC 2010).

2009

• (Roth & Tu, 2009) ⇒ Dan Roth, and Yuancheng Tu. (2009). “Aspect Guided Text Categorization with Unobserved Labels.” In: Proceedings of the Ninth IEEE International Conference on Data Mining (ICDM 2009). doi:10.1109/ICDM.2009.129
• (Ratinov & Roth, 2009) ⇒ Lev Ratinov, and Dan Roth. (2009). “Design Challenges and Misconceptions in Named Entity Recognition.” In: Proceedings of the Thirteenth Conference on Computational Natural Language Learning.

2008

• (Bengtson & Roth, 2008) ⇒ Eric Bengtson, and Dan Roth. (2008). “Understanding the Value of Features for Coreference Resolution.” In: Proceedings of the Conference on Empirical Methods in Natural Language Processing.
• (Chang et al., 2008) ⇒ Ming-Wei Chang, Lev-Arie Ratinov, Dan Roth, and Vivek Srikumar. (2008). “Importance of Semantic Representation: Dataless Classification.” In: AAAI, 2.

2007

• (Chang et al., 2007) ⇒ Ming-Wei Chang, Lev Ratinov, and Dan Roth. (2007). “Guiding Semi-Supervision with Constraint-Driven Learning.” In: Proceedings of the Annual Meeting of the ACL (ACL 2007).

2006

• (Klementiev & Roth, 2006) ⇒ Alexandre Klementiev, and Dan Roth. (2006). “Weakly Supervised Named Entity Transliteration and Discovery from Multilingual Comparable Corpora.” In: Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual Meeting of the Association for Computational Linguistics (ACL 2006). doi:10.3115/1220175.1220278

2005

• (Punyakanok et al., 2005) ⇒ Vasin Punyakanok, Dan Roth, and Wen-tau Yih. (2005). “The Necessity of Syntactic Parsing for Semantic Role Labeling.” In: Proceedings of the 19th international joint conference on Artificial intelligence.

2004

• (Li et al., 2004) ⇒ Xin Li, Paul Morie, and Dan Roth. (2004). “Identification and Tracing of Ambiguous Names: Discriminative and Generative Approaches.” In: Proceedings of AAAI 2004.
• (Roth & Yih, 2004) ⇒ Dan Roth, and Wen-tau Yih. (2004). “A linear programming formulation for global inference in natural language."
• (Punyakanok et al., 2004) ⇒ V. Punyakanok, Dan Roth, Wen-tau Yih, and D. Zimak. (2004). “Semantic role labeling via integer linear programming inference.” In: Proceedings of the 20th International Conference on Computational Linguistics.
• (Agarwal et al., 2004) ⇒ Shivani Agarwal, Aatif Awan, and Dan Roth. (2004). “Learning to Detect Objects in Images via a Sparse, Part-based Representation.” In: IEEE Transactions on pattern analysis and machine intelligence 26, no. 11

2003

• (Cumby & Roth, 2003a) ⇒ Chad Cumby, and Dan Roth. (2003). “Feature Extraction Languages for Propositionalized Relational Learning.” In: IJCAI Workshop on Learning Statistical Models from Relational Data.
• (Cumby & Roth, 2003b) ⇒ Chad Cumby, and Dan Roth. (2003). “Feature extraction languages for propositionalized relational learning.” Technical Report UIUCDCS-R-2003-2346, UIUC Computer Science Department, May 2003.
• (Cumby and Roth, 2003c) ⇒ Chad Cumby, and Dan Roth. (2003). “On Kernel Methods for Relational Learning.” In: Proceedings of ICML Conference (ICML 2003).

2002

• (Roth & Yih, 2002) ⇒ Dan Roth, and Wen-tau Yih. (2002). “Probabilistic Reasoning for Entity & Relation Recognition.” In: Proceedings of the 20th International Conference on Computational Linguistics (COLING 2002).
• (Cumby & Roth, 2002) ⇒ Chad Cumby, and Dan Roth. (2002). “Learning with Feature Description Logics.” In: Proceedings of ILP’02, 2002.

2001

• (Punyakanok & Roth, 2001) ⇒ Vasin Punyakanok, and Dan Roth. (2001). “The Use of Classifiers in Sequential Inference.” In: Proceedings of the 2000 Conference on Advances in Neural Information Processing Systems (NIPS 13).
• (Roth & Yih, 2001) ⇒ Dan Roth, and Wen-tau Yih. (2001). “Relational Learning via Propositional Algorithms: An information extraction case study.” In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI 2001).

2000

• (Tjong et al., 2000) ⇒ Erik F. Tjong Kim Sang, Walter Daelemans, Hervé Déjean, Rob Koeling, Yuval Krymolowski, Vasin Punyakanok, and Dan Roth. (2000). “Applying System Combination to Base Noun Phrase Identification.” In: Proceedings of the 18th conference on Computational Linguistics.
  • QUOTE: We use seven machine learning algorithms for one task: identifying base noun phrases. The results have been processed by different system combination methods and all of these outperformed the best individual result. We have applied the seven learners with the best combinator, a majority vote of the top five systems, to a standard data set and managed to improve the best published result for this data set.
• (Cumby & Roth, 2000) ⇒ [[Chad Cumby] and Dan Roth. (2000). “Relational Representations that Facilitate Learning.” In: Proceedings of KR 2000.
• (Punyakanok et al., 2000) ⇒ Vasin Punyakanok, and Dan Roth. (2000). “The Use of Classifiers in Sequential Inference." Technical Report, University of Illinois at Urbana-Champaign Champaign.

1998

• (Roth, 1998) ⇒ Dan Roth. (1998). “Learning to Resolve Natural Language Ambiguities: A unified approach.” In: Proceedings of National Conference on Artificial Intelligence.

1996

• (Roth, 1996) ⇒ Dan Roth. (1996). “On the Hardness of Approximate Reasoning.” In: Artificial Inteligence, 82(1-2).

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