Filter Results:
- Full text PDF available (14)
Publication Year
2009
2017
- This year (1)
- Last 5 years (12)
- Last 10 years (14)
Publication Type
Co-author
Journals and Conferences
Data Set Used
Key Phrases
Learn More
- Ashish Vaswani, Yonatan Bisk, Kenji Sagae, Ryan Musa
- HLT-NAACL
- 2016
In this paper we present new state-of-the-art performance on CCG supertagging and parsing. Our model outperforms existing approaches by an absolute gain of 1.5%. We analyze the performance of several neural models and demonstrate that while feed-forward architectures can compete with bidirectional LSTMs on POS tagging, models that encode the complete… (More)
- Yonatan Bisk, Deniz Yuret, Daniel Marcu
- HLT-NAACL
- 2016
We propose a framework for devising empirically testable algorithms for bridging the communication gap between humans and robots. We instantiate our framework in the context of a problem setting in which humans give instructions to robots using unrestricted natural language commands, with instruction sequences being subservient to building complex goal… (More)
- Yonatan Bisk, Julia Hockenmaier
- AAAI
- 2012
We present a simple EM-based grammar induction algorithm for Combinatory Categorial Grammar (CCG) that achieves state-of-the-art performance by relying on a minimal number of very general linguistic principles. Unlike previous work on unsupervised parsing with CCGs, our approach has no prior language-specific knowledge, and discovers all categories… (More)
- Yonatan Bisk, Julia Hockenmaier
- TACL
- 2013
We introduce a novel nonparametric Bayesian model for the induction of Combinatory Categorial Grammars from POS-tagged text. It achieves state of the art performance on a number of languages, and induces linguistically plausible lexicons.
- Yonatan Bisk, Daniel Marcu, William Wong
- AAAI Workshop: Symbiotic Cognitive Systems
- 2016
The Natural Language Processing, Artificial Intelligence, and Robotics fields have made significant progress towards developing robust component technologies (speech recognition/synthesis, machine translation, image recognition); advanced inference mechanisms that accommodate uncertainty and noise; and autonomous driving systems that operate seamlessly on… (More)
Our system consists of a simple, EM-based induction algorithm (Bisk and Hockenmaier, 2012), which induces a language-specific Combinatory Categorial grammar (CCG) and lexicon based on a small number of linguistic principles, e.g. that verbs may be the roots of sentences and can take nouns as arguments.
- Tim Weninger, Yonatan Bisk, Jiawei Han
- CIKM
- 2012
Topic taxonomies present a multi-level view of a document collection, where general topics live towards the top of the taxonomy and more specific topics live towards the bottom. Topic taxonomies allow users to quickly drill down into their topic of interest to find documents. We show that hierarchies of documents, where documents live at the inner nodes of… (More)
- Julia Hockenmaier, Yonatan Bisk
- COLING
- 2010
We propose and implement a modification of the Eisner (1996) normal form to account for generalized composition of bounded degree, and an extension to deal with grammatical type-raising.
- Yonatan Bisk, Julia Hockenmaier
- ACL
- 2015
Work in grammar induction should help shed light on the amount of syntactic structure that is discoverable from raw word or tag sequences. But since most current grammar induction algorithms produce unlabeled dependencies, it is difficult to analyze what types of constructions these algorithms can or cannot capture, and, therefore, to identify where… (More)
- Ke Tran, Yonatan Bisk, Ashish Vaswani, Daniel Marcu, Kevin Knight
- ArXiv
- 2016
In this work, we present the first results for neuralizing an Unsupervised Hidden Markov Model. We evaluate our approach on tag induction. Our approach outperforms existing generative models and is competitive with the state-of-the-art though with a simpler model easily extended to include additional context.