O
Owen Rambow
Researcher at Columbia University
Publications - 241
Citations - 10578
Owen Rambow is an academic researcher from Columbia University. The author has contributed to research in topics: Parsing & Tree-adjoining grammar. The author has an hindex of 48, co-authored 241 publications receiving 9838 citations. Previous affiliations of Owen Rambow include University of Massachusetts Amherst & AT&T.
Papers
More filters
Sentiment Analysis of Twitter Data
TL;DR: This article introduced POS-specific prior polarity features and explored the use of a tree kernel to obviate the need for tedious feature engineering for sentiment analysis on Twitter data, which outperformed the state-of-the-art baseline.
Proceedings Article
MADAMIRA: A Fast, Comprehensive Tool for Morphological Analysis and Disambiguation of Arabic
Arfath Pasha,Mohamed Al-Badrashiny,Mona Diab,Ahmed El Kholy,Ramy Eskander,Nizar Habash,Manoj Pooleery,Owen Rambow,Ryan M. Roth +8 more
TL;DR: MADAMIRA is a system for morphological analysis and disambiguation of Arabic that combines some of the best aspects of two previously commonly used systems for Arabic processing with a more streamlined Java implementation that is more robust, portable, extensible, and is faster than its ancestors by more than an order of magnitude.
Proceedings ArticleDOI
Arabic Tokenization, Part-of-Speech Tagging and Morphological Disambiguation in One Fell Swoop
Nizar Habash,Owen Rambow +1 more
TL;DR: An approach to using a morphological analyzer for tokenizing and morphologically tagging Arabic words in one process using classifiers for individual morphological features, as well as ways of using these classifiers to choose among entries from the output of the analyzer.
Proceedings Article
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Proceedings ArticleDOI
Exploiting a probabilistic hierarchical model for generation
Srinivas Bangalore,Owen Rambow +1 more
TL;DR: Initial results are presented showing that a tree-based model derived from aTree-annotated corpus improves on a tree modelderived from an unannotated Corpus, and that a Tree-based stochastic model with a hand-crafted grammar outperforms both.